SYSTEMS AND METHODS FOR AUTONOMOUS FLIGHT COLLISION AVOIDANCE IN AN ELECTRIC AIRCRAFT

A system for autonomous flight collision avoidance in ana electric aircraft, where the system includes an electric aircraft. The electric aircraft includes a at least a sensor coupled to the electric aircraft, where the at least a sensor coupled to the aircraft is configured to detect an obstacle in the electric aircraft's flight path and transmit the obstacle to a flight controller. The electric aircraft also includes a flight controller where the flight controller is configured to receive the obstacle from the at least a sensor coupled to the electric aircraft, determine an adjusted flight path as a function of the obstacle, and transmit the adjusted flight path to a pilot display. The system further includes a pilot display, where the pilot display is configured to receive the adjusted flight path form the flight controller and display the adjusted flight path to a user.

FIELD OF THE INVENTION

The present invention generally relates to the field of electric aircraft. In particular, the present invention is directed to systems and methods for autonomous flight collision avoidance in an electric aircraft.

BACKGROUND

In an autonomous electric aircraft flight, objects, such as another aircraft, may not be present in the predetermined flight path. In a situation when an unexpected object in the flight path is detected, a new flight path may be required as to avoid a collision with the object.

SUMMARY OF THE DISCLOSURE

In an aspect a system for autonomous flight collision avoidance in an electric aircraft, where the system includes an electric aircraft, where the electric aircraft further includes at least a sensor coupled to the electric aircraft, where the at least a sensor is configured to detect an obstacle in the electric aircraft's flight path and transmit the obstacle to the flight controller, and a flight controller where the flight controller if configured to receive the obstacle from the at least a sensor coupled to the electric aircraft, determine an adjusted flight path as a function of the obstacle, and transmit the adjusted flight path to a pilot display. The system further includes a pilot display, where the pilot display is configured to receive the adjusted flight path for the flight controller and display the adjusted flight path to a user.

In another aspect a method for autonomous flight collision avoidance in an electric aircraft, the method including detecting, by at least a sensor coupled to the electric aircraft, an obstacle in the electric aircraft's flight path; transmitting, by the at least a sensor coupled to the electric aircraft, the obstacle to a flight controller; receiving, by the flight controller, the obstacle from the at least a sensor coupled to the electric aircraft; determining, by the flight controller, an adjusted flight path as a function of the obstacle; transmitting, by the flight controller, the adjusted flight path to a pilot display; receiving, by the pilot display, the adjusted flight path from the flight controller; and displaying, by the pilot display, the adjusted flight path to a user.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to systems and methods for autonomous flight collision avoidance in an electric aircraft. In an embodiment, a system for autonomous flight collision avoidance in an electric aircraft, where the system includes an electric aircraft where the electric aircraft further includes at least a sensor coupled to the electric aircraft configured to detect an obstacle in the electric aircraft's flight path and transmit the obstacle to a flight controller. The electric aircraft also includes a flight controller configured to receive the obstacle from the at least a sensor coupled to the electric aircraft, determine an adjusted flight path as a function of the obstacle, and transmit the adjusted flight oath to a pilot display. The system further includes a pilot display configured to receive the adjusted flight path from the flight controller and display the adjusted flight path to a user.

Aspects of the present disclosure can be used to adjust the flight path of an autonomous electric aircraft based on objects detected by the aircraft. Aspects of the present disclosure can also be used to allow a user to select a desired adjusted flight path for the autonomous aircraft to follow. This is so, at least in part, because the system calculates at least one adjusted flight path when the system encounters an obstacle in the original flight path and transmits at least one flight path to a user. Aspects of this disclosure can also be used to require a user to give some tactile feedback, as to show the system that the user is there, and if no tactile feedback is detected within a set amount of time, the system will automatically follow an adjusted flight path.

Aspects of the present disclosure allow for an autonomous electric aircraft to calculate a new flight path when the system detects an obstacle, and follow the adjusted flight path as to avoid the obstacle. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

Referring now toFIG.1, an exemplary embodiment of a system100for collision avoidance in autonomous flight in an electric aircraft is illustrated. System100includes at least a sensor104coupled to the electric aircraft. At least a sensor104coupled to the electric aircraft may include a motion sensor. “Motion sensor”, for the purposes of this disclosure refers to a device or component configured to detect physical movement of an object or grouping of objects. One of ordinary skill in the art would appreciate, after reviewing the entirety of this disclosure, that motion may include a plurality of types including but not limited to: spinning, rotating, oscillating, gyrating, jumping, sliding, reciprocating, or the like. At least a sensor104coupled to the electric aircraft may include, torque sensor, gyroscope, accelerometer, torque sensor, magnetometer, inertial measurement unit (IMU), pressure sensor, force sensor, proximity sensor, displacement sensor, vibration sensor, among others. At least a sensor104coupled to the electric aircraft may include a sensor suite which may include a plurality of sensors that may detect similar or unique phenomena. For example, in a non-limiting embodiment, sensor suite may include a plurality of accelerometers, a mixture of accelerometers and gyroscopes, or a mixture of an accelerometer, gyroscope, and torque sensor. The herein disclosed system and method may comprise a plurality of sensors in the form of individual sensors or a sensor suite working in tandem or individually. A sensor suite may include a plurality of independent sensors, as described herein, where any number of the described sensors may be used to detect any number of physical or electrical quantities associated with an aircraft power system or an electrical energy storage system. Independent sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as a user graphical interface. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability to detect phenomenon is maintained and in a non-limiting example, a user alter aircraft usage pursuant to sensor readings. In another nonlimiting example, a flight controller alters aircraft usage pursuant to sensor readings.

Continuing to refer toFIG.1, at least a sensor104may further include, without limitation, a device that performs radio detection and ranging (RADAR), a device that performs lidar, a device that performs sound navigation ranging (SONAR), an optical device such as a camera, electro-optical (EO) sensors that produce images that mimic human sight, or any other sensor104that may occur to a person having ordinary skill in the art upon. At least a sensor104may include a sense-and-avoidance system (SAA) that detects and avoids collisions. SAA may include traffic collision avoidance system (TCAS) and ground-based sense-and-avoid (GBSAA) using primary radar. SAA may operate one or more protocols requiring the aircraft to remain well clear from and avoid collisions with other airborne traffic. SAA may also perform one or more protocols to compensate for the lack of an on-board pilot, and to define an operational concept that will enable the SAA-equipped aircraft to smoothly integrate into an air traffic services environment.

Alternatively, or additionally, and still referring toFIG.1. At least a sensor104may include an environmental sensor. As used herein, an environmental sensor may be used to detect ambient temperature, barometric pressure, air velocity, motion sensors which may include gyroscopes, accelerometers, inertial measurement unit (IMU), various magnetic, humidity, and/or oxygen. As another non-limiting example, at least a sensor104may include a geospatial sensor. As used herein, a geospatial sensor may include optical/radar/Lidar, GPS and may be used to detect aircraft location, aircraft speed, aircraft altitude and whether the aircraft is on the correct location of the flight plan. An environmental sensor may further collect environmental information from the predetermined area, such as ambient temperature, barometric pressure, air velocity, motion sensors which may include gyroscopes, accelerometers, inertial measurement unit (IMU), various magnetic, humidity, and/or oxygen. The information may be collected from outside databases and/or information services, such as Aviation Weather Information Services. Local sensor may detect an environmental parameter, a temperature, a barometric pressure, a location parameter, and/or other necessary measurements.

Still referring toFIG.1, at least a sensor104coupled to the electric aircraft is configured to detect an obstacle in the electric aircraft's flight path. “Obstacle” for the purposes of this disclosure may be an elevation in a topographical area, any inanimate object, such as a mountain or trees, any moving objects, such as a flock of birds, an aircraft not equipped with a tracker, or with the tracker disabled, weather phenomena, such as a tornado, and the like. “Flight path” for the purposes of this disclosure is a predefined path instruction for the aircraft to follow.

Continuing to refer toFIG.1, at least a sensor104is further configured to generate an obstacle detection datum as a function of the obstacle. At least a sensor104may include circuitry, computing devices, electronic components or a combination thereof that translates data related to obstacle detected into at least an electronic signal configured to be transmitted to another electronic component.

Still referring toFIG.1, the system100includes the flight controller108, where the flight controller108is configured to receive the obstacle detection datum from the at least a sensor104coupled to the electric aircraft. Flight controller108is described in detail further below. In one embodiment, flight controller108may receive descriptive data related to the obstacle from the at least a sensor104coupled to the electric aircraft.

Alternatively, or additionally, flight controller108may be a proportional-integral-derivative (PID) controller. For the purpose of this disclosure and as a nonlimiting example, proportional-integral-derivative (PID) controller may utilize the following formula:

Where u(t) is the drive coming from the Controller, into the Process, at time t, e(t)=ysp(t)−y(t) is the difference between the setpoint and measured process variable at time t, and Kp, Ki, Kdare the respective Proportional, Integral, and Derivative constants.

Continuing to refer toFIG.1, the flight controller108is further configured to determine an adjusted flight path as a function of the obstacle. “Adjusted flight path”, for the purposes of this disclosure, may include at least one flight path calculated as a function of the detection of at least an obstacle. In one embodiment, adjusted flight path may include a plurality of flight paths, where the flight controller108may follow a flight path based on a plurality of data, such as weather information. In a nonlimiting example, flight controller108may calculate multiple adjusted flight paths based on an obstacle and follow the flight path with the shortest route. In another nonlimiting example, flight controller108may follow an adjusted flight path, from a plurality of adjusted flight paths, based on weather information. “Follow a flight path”, for the purposes of this disclosure, may include the flight controller108directly controlling the electric aircraft as a function of the flight path.

Alternatively, or additionally, and still referring toFIG.1, flight controller108may be further configured to perform a maneuver as to avoid an obstacle. In one embodiment, flight controller108may change the predetermined flight path to the adjusted flight path and follow the adjusted path, without interaction with the user. In some embodiments, flight controller108may be configured to determine a projected flight path of a moving obstacle. In some embodiments, flight controller108may determine an adjusted flight path as a function of a moving obstacle and the projected flight path of the moving obstacle. In a nonlimiting example, at least a sensor104coupled to the electric aircraft may only detect an obstacle within a short distance of the electric aircraft, where the flight controller108may perform a maneuver as to avoid hitting the obstacle. In another nonlimiting example, flight controller may calculate a projected flight path of an approaching aircraft and determine an adjusted flight path based on the projected path of the other aircraft.

Continuing to refer toFIG.1, the flight controller108is further configured to transmit the adjusted flight path to a pilot display112. “Pilot display” for the purposes for the purpose of this disclosure refers to any computing device configured to display data to a user. Computing device is described in detail further below. As an example, and without limitation, adjusted flight plan may be displayed on any electronic device, as described herein, such as, without limitation, a computer, tablet, remote device, and/or any other visual display device. Pilot display112is configured to present, to a user, information related to the flight path. Pilot display112may include a graphical user interface, multi-function display (MFD), primary display, gauges, graphs, audio cues, visual cues, information on a heads-up display (HUD) or a combination thereof. Pilot display112may include a display disposed in one or more areas of an aircraft, on a user device remotely located, one or more computing devices, or a combination thereof. Pilot display112may be disposed in a projection, hologram, or screen within a user's helmet, eyeglasses, contact lens, or a combination thereof. Pilot display112may display the flight plan in graphical form. Graphical form may include a two-dimensional plot of two variables that represent data received by the flight controller108, such as original flight path and adjusted flight path. In one embodiment, Pilot display112may also display the a graphical representation of the obstacle in real-time. In a nonlimiting example, flight controller may transmit the adjusted flight path to a pilot display112located inside the aircraft. In another nonlimiting example, flight controller108may transmit adjusted flight path to a remote computing device, such as a user's laptop. In yet another nonlimiting example, flight controller108may transmit the adjusted flight path to a remote server.

Alternatively, or additionally, the flight controller108may transmit a prompt for a tactile feedback from the user. In one embodiment, flight controller108may automatically follow the adjusted flight path if no tactile feedback is received within a set amount of time.

Still referring toFIG.1, system100includes the pilot display112, where the pilot display112is configured to receive the adjusted flight path from the flight controller. In one embodiment, pilot display112may also receive a graphical representation of the obstacle from the flight controller108. In some embodiments, pilot display112may further receive descriptive data related to the obstacle from the flight controller108.

Continuing to refer toFIG.1, pilot display112is further configured to display the adjusted flight path to a user. In one embodiment, adjusted flight path may include a plurality of flight paths for the user to choose from. In a nonlimiting example, a user may be able to select from a list of flight paths displayed. In another nonlimiting example, a user may click, through a touchscreen, on the desired flight path from a plurality of flight paths.

Now referring toFIG.2, an exemplary illustration of a method200for collision avoidance in autonomous flight in an electric aircraft is presented. At step205, method includes detecting, by at least a sensor104coupled to the electric aircraft, an obstacle in the electric aircraft's flight path.

Still referring toFIG.2, at step210, method200includes generating, by the at least a sensor104coupled to the electric aircraft, an obstacle detection datum as a function of the detection of the obstacle. In one embodiment, method may include transmitting, by the at least a sensor104coupled to the electric aircraft, descriptive data of the obstacle.

Continuing to refer toFIG.2, at step215, method200includes receiving, by the flight controller108, the obstacle detection datum from the at least a sensor104coupled to the electric aircraft.

Still referring toFIG.2, at step220, method200includes determining, by the flight controller108, an adjusted flight path as a function of the obstacle. In some embodiments, method200may include performing a maneuver as a function of the obstacle. In some embodiments, method200may further include operating, by the flight controller, the electric aircraft as a function of the adjusted flight path.

Alternatively, or additionally, method200may include prompting the user for a tactile feedback. In some embodiments, method200may include operating, by the flight controller108, the electric aircraft in the absence of the tactile feedback within a set amount of time. In one embodiment, method200may include calculating, by the flight controller108, a projected flight path of a moving obstacle. In some embodiments, method200may include utilizing proportional-integral-derivative (PID) control.

Continuing to referFIG.2, at step225, method200includes transmitting, by the flight controller108, the adjusted flight path to a pilot display112. In some embodiments, method200may include transmitting, by the fight controller, a graphical representation of the obstacle to the pilot display112. In one embodiment, method200may include transmitting, by the fight controller108, the descriptive data of the obstacle.

Still referring toFIG.2, at step230, method200includes receiving, by the pilot display112, the adjusted flight path from the flight controller108.

Continuing to refer toFIG.2, at step235, method200includes displaying, by the pilot display112, the adjusted flight path to a user.

Now referring toFIG.3, an exemplary representation of the flight controller108determining an adjusted flight path as a function of the obstacle is illustrated. In a nonlimiting example, the flight controller108calculates a flight path304for an electric aircraft308to follow with information available at the time of the calculation and once at least a sensor104detects an obstacle312in the flight path304, the flight controller108will calculate an adjusted flight path316that avoids the obstacle. In one embodiment, the flight controller108may adjust a section of the flight path304as to avoid an obstacle312. In some embodiments, the flight controller108may calculate an adjusted flight path312that is completely a new flight path108based on at least an obstacle and a plurality of other factors discussed in this disclosure.

Now referring toFIG.4, an exemplary embodiment400of a flight controller404is illustrated. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller108may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller108may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller108may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith.

In an embodiment, and still referring toFIG.4, flight controller108may include a signal transformation component404. As used in this disclosure a “signal transformation component” is a component that transforms and/or converts a first signal to a second signal, wherein a signal may include one or more digital and/or analog signals. For example, and without limitation, signal transformation component404may be configured to perform one or more operations such as preprocessing, lexical analysis, parsing, semantic analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component404may include one or more analog-to-digital convertors that transform a first signal of an analog signal to a second signal of a digital signal. For example, and without limitation, an analog-to-digital converter may convert an analog input signal to a 10-bit binary digital representation of that signal. In another embodiment, signal transformation component404may include transforming one or more low-level languages such as, but not limited to, machine languages and/or assembly languages. For example, and without limitation, signal transformation component404may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component404may include transforming one or more high-level languages and/or formal languages such as but not limited to alphabets, strings, and/or languages. For example, and without limitation, high-level languages may include one or more system languages, scripting languages, domain-specific languages, visual languages, esoteric languages, and the like thereof. As a further non-limiting example, high-level languages may include one or more algebraic formula languages, business data languages, string and list languages, object-oriented languages, and the like thereof

Still referring toFIG.4, signal transformation component404may be configured to optimize an intermediate representation408. As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component404may optimize intermediate representation as a function of a data-flow analysis, dependence analysis, alias analysis, pointer analysis, escape analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component404may optimize intermediate representation408as a function of one or more inline expansions, dead code eliminations, constant propagation, loop transformations, and/or automatic parallelization functions. In another embodiment, signal transformation component404may optimize intermediate representation as a function of a machine dependent optimization such as a peephole optimization, wherein a peephole optimization may rewrite short sequences of code into more efficient sequences of code. Signal transformation component404may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller108. For example, and without limitation, native machine language may include one or more binary and/or numerical languages.

In an embodiment, and without limitation, signal transformation component404may include transform one or more inputs and outputs as a function of an error correction code. An error correction code, also known as error correcting code (ECC), is an encoding of a message or lot of data using redundant information, permitting recovery of corrupted data. An ECC may include a block code, in which information is encoded on fixed-size packets and/or blocks of data elements such as symbols of predetermined size, bits, or the like. Reed-Solomon coding, in which message symbols within a symbol set having q symbols are encoded as coefficients of a polynomial of degree less than or equal to a natural number k, over a finite field F with q elements; strings so encoded have a minimum hamming distance of k+1, and permit correction of (q−k−1)/2 erroneous symbols. Block code may alternatively or additionally be implemented using Golay coding, also known as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-check coding, and/or Hamming codes. An ECC may alternatively or additionally be based on a convolutional code.

In an embodiment, and still referring toFIG.4, flight controller108may include a reconfigurable hardware platform412. A “reconfigurable hardware platform,” as used herein, is a component and/or unit of hardware that may be reprogrammed, such that, for instance, a data path between elements such as logic gates or other digital circuit elements may be modified to change an algorithm, state, logical sequence, or the like of the component and/or unit. This may be accomplished with such flexible high-speed computing fabrics as field-programmable gate arrays (FPGAs), which may include a grid of interconnected logic gates, connections between which may be severed and/or restored to program in modified logic. Reconfigurable hardware platform412may be reconfigured to enact any algorithm and/or algorithm selection process received from another computing device and/or created using machine-learning processes.

Still referring toFIG.4, reconfigurable hardware platform412may include a logic component416. As used in this disclosure a “logic component” is a component that executes instructions on output language. For example, and without limitation, logic component may perform basic arithmetic, logic, controlling, input/output operations, and the like thereof. Logic component416may include any suitable processor, such as without limitation a component incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; logic component416may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component416may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC). In an embodiment, logic component416may include one or more integrated circuit microprocessors, which may contain one or more central processing units, central processors, and/or main processors, on a single metal-oxide-semiconductor chip. Logic component416may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation408. Logic component416may be configured to fetch and/or retrieve the instruction from a memory cache, wherein a “memory cache,” as used in this disclosure, is a stored instruction set on flight controller108. Logic component416may be configured to decode the instruction retrieved from the memory cache to opcodes and/or operands. Logic component416may be configured to execute the instruction on intermediate representation408and/or output language. For example, and without limitation, logic component416may be configured to execute an addition operation on intermediate representation408and/or output language.

In an embodiment, and without limitation, logic component416may be configured to calculate a flight element420. As used in this disclosure a “flight element” is an element of datum denoting a relative status of aircraft. For example, and without limitation, flight element420may denote one or more torques, thrusts, airspeed velocities, forces, altitudes, groundspeed velocities, directions during flight, directions facing, forces, orientations, and the like thereof. For example, and without limitation, flight element420may denote that aircraft is cruising at an altitude and/or with a sufficient magnitude of forward thrust. As a further non-limiting example, flight status may denote that is building thrust and/or groundspeed velocity in preparation for a takeoff. As a further non-limiting example, flight element420may denote that aircraft is following a flight path accurately and/or sufficiently.

Still referring toFIG.4, flight controller108may include a chipset component424. As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component424may include a northbridge data flow path, wherein the northbridge dataflow path may manage data flow from logic component416to a high-speed device and/or component, such as a RAM, graphics controller, and the like thereof. In another embodiment, and without limitation, chipset component424may include a southbridge data flow path, wherein the southbridge dataflow path may manage data flow from logic component416to lower-speed peripheral buses, such as a peripheral component interconnect (PCI), industry standard architecture (ICA), and the like thereof. In an embodiment, and without limitation, southbridge data flow path may include managing data flow between peripheral connections such as ethernet, USB, audio devices, and the like thereof. Additionally or alternatively, chipset component424may manage data flow between logic component416, memory cache, and a flight component428. As used in this disclosure a “flight component” is a portion of an aircraft that can be moved or adjusted to affect one or more flight elements. For example, flight component428may include a component used to affect the aircrafts' roll and pitch which may comprise one or more ailerons. As a further example, flight component428may include a rudder to control yaw of an aircraft. In an embodiment, chipset component424may be configured to communicate with a plurality of flight components as a function of flight element420. For example, and without limitation, chipset component424may transmit to an aircraft rotor to reduce torque of a first lift propulsor and increase the forward thrust produced by a pusher component to perform a flight maneuver.

In an embodiment, and still referring toFIG.4, flight controller108may be configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller108that controls aircraft automatically. For example, and without limitation, autonomous function may perform one or more aircraft maneuvers, take offs, landings, altitude adjustments, flight leveling adjustments, turns, climbs, and/or descents. As a further non-limiting example, autonomous function may adjust one or more airspeed velocities, thrusts, torques, and/or groundspeed velocities. As a further non-limiting example, autonomous function may perform one or more flight path corrections and/or flight path modifications as a function of flight element420. In an embodiment, autonomous function may include one or more modes of autonomy such as, but not limited to, autonomous mode, semi-autonomous mode, and/or non-autonomous mode. As used in this disclosure “autonomous mode” is a mode that automatically adjusts and/or controls aircraft and/or the maneuvers of aircraft in its entirety. For example, autonomous mode may denote that flight controller108will adjust the aircraft. As used in this disclosure a “semi-autonomous mode” is a mode that automatically adjusts and/or controls a portion and/or section of aircraft. For example, and without limitation, semi-autonomous mode may denote that a pilot will control the propulsors, wherein flight controller108will control the ailerons and/or rudders. As used in this disclosure “non-autonomous mode” is a mode that denotes a pilot will control aircraft and/or maneuvers of aircraft in its entirety.

In an embodiment, and still referring toFIG.4, flight controller108may generate autonomous function as a function of an autonomous machine-learning model. As used in this disclosure an “autonomous machine-learning model” is a machine-learning model to produce an autonomous function output given flight element420and a pilot signal432as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. As used in this disclosure a “pilot signal” is an element of datum representing one or more functions a pilot is controlling and/or adjusting. For example, pilot signal432may denote that a pilot is controlling and/or maneuvering ailerons, wherein the pilot is not in control of the rudders and/or propulsors. In an embodiment, pilot signal432may include an implicit signal and/or an explicit signal. For example, and without limitation, pilot signal432may include an explicit signal, wherein the pilot explicitly states there is a lack of control and/or desire for autonomous function. As a further non-limiting example, pilot signal432may include an explicit signal directing flight controller108to control and/or maintain a portion of aircraft, a portion of the flight plan, the entire aircraft, and/or the entire flight plan. As a further non-limiting example, pilot signal432may include an implicit signal, wherein flight controller108detects a lack of control such as by a malfunction, torque alteration, flight path deviation, and the like thereof. In an embodiment, and without limitation, pilot signal432may include one or more explicit signals to reduce torque, and/or one or more implicit signals that torque may be reduced due to reduction of airspeed velocity. In an embodiment, and without limitation, pilot signal432may include one or more local and/or global signals. For example, and without limitation, pilot signal432may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot signal432may include a global signal that is transmitted by air traffic control and/or one or more remote users that are in communication with the pilot of aircraft. In an embodiment, pilot signal432may be received as a function of a tri-state bus and/or multiplexor that denotes an explicit pilot signal should be transmitted prior to any implicit or global pilot signal.

Still referring toFIG.4, autonomous machine-learning model may include one or more autonomous machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that flight controller108and/or a remote device may or may not use in the generation of autonomous function. As used in this disclosure “remote device” is an external device to flight controller108. Additionally or alternatively, autonomous machine-learning model may include one or more autonomous machine-learning processes that a field-programmable gate array (FPGA) may or may not use in the generation of autonomous function. Autonomous machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elastic net regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naive bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

In an embodiment, and still referring toFIG.4, autonomous machine learning model may be trained as a function of autonomous training data, wherein autonomous training data may correlate a flight element, pilot signal, and/or simulation data to an autonomous function. For example, and without limitation, a flight element of an airspeed velocity, a pilot signal of limited and/or no control of propulsors, and a simulation data of required airspeed velocity to reach the destination may result in an autonomous function that includes a semi-autonomous mode to increase thrust of the propulsors. Autonomous training data may be received as a function of user-entered valuations of flight elements, pilot signals, simulation data, and/or autonomous functions. Flight controller108may receive autonomous training data by receiving correlations of flight element, pilot signal, and/or simulation data to an autonomous function that were previously received and/or determined during a previous iteration of generation of autonomous function. Autonomous training data may be received by one or more remote devices and/or FPGAs that at least correlate a flight element, pilot signal, and/or simulation data to an autonomous function. Autonomous training data may be received in the form of one or more user-entered correlations of a flight element, pilot signal, and/or simulation data to an autonomous function.

Still referring toFIG.4, flight controller108may receive autonomous machine-learning model from a remote device and/or FPGA that utilizes one or more autonomous machine learning processes, wherein a remote device and an FPGA is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, FPGA, microprocessor and the like thereof. Remote device and/or FPGA may perform the autonomous machine-learning process using autonomous training data to generate autonomous function and transmit the output to flight controller108. Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller108that at least relates to autonomous function. Additionally or alternatively, the remote device and/or FPGA may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, a autonomous machine-learning process correction, and the like thereof. As a non-limiting example a software update may incorporate a new simulation data that relates to a modified flight element. Additionally or alternatively, the updated machine learning model may be transmitted to the remote device and/or FPGA, wherein the remote device and/or FPGA may replace the autonomous machine-learning model with the updated machine-learning model and generate the autonomous function as a function of the flight element, pilot signal, and/or simulation data using the updated machine-learning model. The updated machine-learning model may be transmitted by the remote device and/or FPGA and received by flight controller108as a software update, firmware update, or corrected autonomous machine-learning model. For example, and without limitation autonomous machine learning model may utilize a neural net machine-learning process, wherein the updated machine-learning model may incorporate a gradient boosting machine-learning process.

Still referring toFIG.4, flight controller108may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Further, flight controller may communicate with one or more additional devices as described below in further detail via a network interface device. The network interface device may be utilized for commutatively connecting a flight controller to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. The network may include any network topology and can may employ a wired and/or a wireless mode of communication.

In an embodiment, and still referring toFIG.4, flight controller108may include, but is not limited to, for example, a cluster of flight controllers in a first location and a second flight controller or cluster of flight controllers in a second location. Flight controller108may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller108may be configured to distribute one or more computing tasks as described below across a plurality of flight controllers, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. For example, and without limitation, flight controller108may implement a control algorithm to distribute and/or command the plurality of flight controllers. As used in this disclosure a “control algorithm” is a finite sequence of well-defined computer implementable instructions that may determine the flight component of the plurality of flight components to be adjusted. For example, and without limitation, control algorithm may include one or more algorithms that reduce and/or prevent aviation asymmetry. As a further non-limiting example, control algorithms may include one or more models generated as a function of a software including, but not limited to Simulink by MathWorks, Natick, Mass., USA. In an embodiment, and without limitation, control algorithm may be configured to generate an auto-code, wherein an “auto-code,” is used herein, is a code and/or algorithm that is generated as a function of the one or more models and/or software's. In another embodiment, control algorithm may be configured to produce a segmented control algorithm. As used in this disclosure a “segmented control algorithm” is control algorithm that has been separated and/or parsed into discrete sections. For example, and without limitation, segmented control algorithm may parse control algorithm into two or more segments, wherein each segment of control algorithm may be performed by one or more flight controllers operating on distinct flight components.

In an embodiment, and still referring toFIG.4, control algorithm may be configured to determine a segmentation boundary as a function of segmented control algorithm. As used in this disclosure a “segmentation boundary” is a limit and/or delineation associated with the segments of the segmented control algorithm. For example, and without limitation, segmentation boundary may denote that a segment in the control algorithm has a first starting section and/or a first ending section. As a further non-limiting example, segmentation boundary may include one or more boundaries associated with an ability of flight component428. In an embodiment, control algorithm may be configured to create an optimized signal communication as a function of segmentation boundary. For example, and without limitation, optimized signal communication may include identifying the discrete timing required to transmit and/or receive the one or more segmentation boundaries. In an embodiment, and without limitation, creating optimized signal communication further comprises separating a plurality of signal codes across the plurality of flight controllers. For example, and without limitation the plurality of flight controllers may include one or more formal networks, wherein formal networks transmit data along an authority chain and/or are limited to task-related communications. As a further non-limiting example, communication network may include informal networks, wherein informal networks transmit data in any direction. In an embodiment, and without limitation, the plurality of flight controllers may include a chain path, wherein a “chain path,” as used herein, is a linear communication path comprising a hierarchy that data may flow through. In an embodiment, and without limitation, the plurality of flight controllers may include an all-channel path, wherein an “all-channel path,” as used herein, is a communication path that is not restricted to a particular direction. For example, and without limitation, data may be transmitted upward, downward, laterally, and the like thereof. In an embodiment, and without limitation, the plurality of flight controllers may include one or more neural networks that assign a weighted value to a transmitted datum. For example, and without limitation, a weighted value may be assigned as a function of one or more signals denoting that a flight component is malfunctioning and/or in a failure state.

Still referring toFIG.4, the plurality of flight controllers may include a master bus controller. As used in this disclosure a “master bus controller” is one or more devices and/or components that are connected to a bus to initiate a direct memory access transaction, wherein a bus is one or more terminals in a bus architecture. Master bus controller may communicate using synchronous and/or asynchronous bus control protocols. In an embodiment, master bus controller may include flight controller108. In another embodiment, master bus controller may include one or more universal asynchronous receiver-transmitters (UART). For example, and without limitation, master bus controller may include one or more bus architectures that allow a bus to initiate a direct memory access transaction from one or more buses in the bus architectures. As a further non-limiting example, master bus controller may include one or more peripheral devices and/or components to communicate with another peripheral device and/or component and/or the master bus controller. In an embodiment, master bus controller may be configured to perform bus arbitration. As used in this disclosure “bus arbitration” is method and/or scheme to prevent multiple buses from attempting to communicate with and/or connect to master bus controller. For example and without limitation, bus arbitration may include one or more schemes such as a small computer interface system, wherein a small computer interface system is a set of standards for physical connecting and transferring data between peripheral devices and master bus controller by defining commands, protocols, electrical, optical, and/or logical interfaces. In an embodiment, master bus controller may receive intermediate representation408and/or output language from logic component416, wherein output language may include one or more analog-to-digital conversions, low bit rate transmissions, message encryptions, digital signals, binary signals, logic signals, analog signals, and the like thereof described above in detail.

Still referring toFIG.4, master bus controller may communicate with a slave bus. As used in this disclosure a “slave bus” is one or more peripheral devices and/or components that initiate a bus transfer. For example, and without limitation, slave bus may receive one or more controls and/or asymmetric communications from master bus controller, wherein slave bus transfers data stored to master bus controller. In an embodiment, and without limitation, slave bus may include one or more internal buses, such as but not limited to a/an internal data bus, memory bus, system bus, front-side bus, and the like thereof. In another embodiment, and without limitation, slave bus may include one or more external buses such as external flight controllers, external computers, remote devices, printers, aircraft computer systems, flight control systems, and the like thereof.

In an embodiment, and still referring toFIG.4, control algorithm may optimize signal communication as a function of determining one or more discrete timings. For example, and without limitation master bus controller may synchronize timing of the segmented control algorithm by injecting high priority timing signals on a bus of the master bus control. As used in this disclosure a “high priority timing signal” is information denoting that the information is important. For example, and without limitation, high priority timing signal may denote that a section of control algorithm is of high priority and should be analyzed and/or transmitted prior to any other sections being analyzed and/or transmitted. In an embodiment, high priority timing signal may include one or more priority packets. As used in this disclosure a “priority packet” is a formatted unit of data that is communicated between the plurality of flight controllers. For example, and without limitation, priority packet may denote that a section of control algorithm should be used and/or is of greater priority than other sections.

Still referring toFIG.4, flight controller108may also be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of aircraft and/or computing device. Flight controller108may include a distributer flight controller. As used in this disclosure a “distributer flight controller” is a component that adjusts and/or controls a plurality of flight components as a function of a plurality of flight controllers. For example, distributer flight controller may include a flight controller that communicates with a plurality of additional flight controllers and/or clusters of flight controllers. In an embodiment, distributed flight control may include one or more neural networks. For example, neural network also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring toFIG.4, a node may include, without limitation a plurality of inputs xithat may receive numerical values from inputs to a neural network containing the node and/or from other nodes. Node may perform a weighted sum of inputs using weights withat are multiplied by respective inputs xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight wiapplied to an input ximay indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and/or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights wimay be determined by training a neural network using training data, which may be performed using any suitable process as described above. In an embodiment, and without limitation, a neural network may receive semantic units as inputs and output vectors representing such semantic units according to weights withat are derived using machine-learning processes as described in this disclosure.

Still referring toFIG.4, flight controller may include a sub-controller436. As used in this disclosure a “sub-controller” is a controller and/or component that is part of a distributed controller as described above; for instance, flight controller108may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller436may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller436may include any component of any flight controller as described above. Sub-controller436may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller436may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data across the distributed flight controller as described above. As a further non-limiting example, sub-controller436may include a controller that receives a signal from a first flight controller and/or first distributed flight controller component and transmits the signal to a plurality of additional sub-controllers and/or flight components.

Still referring toFIG.4, flight controller may include a co-controller440. As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller108as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller440may include one or more controllers and/or components that are similar to flight controller108. As a further non-limiting example, co-controller440may include any controller and/or component that joins flight controller108to distributer flight controller. As a further non-limiting example, co-controller440may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data to and/or from flight controller108to distributed flight control system. Co-controller440may include any component of any flight controller as described above. Co-controller440may be implemented in any manner suitable for implementation of a flight controller as described above.

Referring now toFIG.6, an embodiment of an electric aircraft600is presented. Still referring toFIG.6, electric aircraft600may include a vertical takeoff and landing aircraft (eVTOL). As used herein, a vertical take-off and landing (eVTOL) aircraft is one that can hover, take off, and land vertically. An eVTOL, as used herein, is an electrically powered aircraft typically using an energy source, of a plurality of energy sources to power the aircraft. In order to optimize the power and energy necessary to propel the aircraft. eVTOL may be capable of rotor-based cruising flight, rotor-based takeoff, rotor-based landing, fixed-wing cruising flight, airplane-style takeoff, airplane-style landing, and/or any combination thereof. Rotor-based flight, as described herein, is where the aircraft generated lift and propulsion by way of one or more powered rotors coupled with an engine, such as a “quad copter,” multi-rotor helicopter, or other vehicle that maintains its lift primarily using downward thrusting propulsors. Fixed-wing flight, as described herein, is where the aircraft is capable of flight using wings and/or foils that generate life caused by the aircraft's forward airspeed and the shape of the wings and/or foils, such as airplane-style flight.

With continued reference toFIG.6, a number of aerodynamic forces may act upon the electric aircraft600during flight. Forces acting on an electric aircraft600during flight may include, without limitation, thrust, the forward force produced by the rotating element of the electric aircraft600and acts parallel to the longitudinal axis. Another force acting upon electric aircraft600may be, without limitation, drag, which may be defined as a rearward retarding force which is caused by disruption of airflow by any protruding surface of the electric aircraft600such as, without limitation, the wing, rotor, and fuselage. Drag may oppose thrust and acts rearward parallel to the relative wind. A further force acting upon electric aircraft600may include, without limitation, weight, which may include a combined load of the electric aircraft600itself, crew, baggage, and/or fuel. Weight may pull electric aircraft600downward due to the force of gravity. An additional force acting on electric aircraft600may include, without limitation, lift, which may act to oppose the downward force of weight and may be produced by the dynamic effect of air acting on the airfoil and/or downward thrust from the propulsor of the electric aircraft. Lift generated by the airfoil may depend on speed of airflow, density of air, total area of an airfoil and/or segment thereof, and/or an angle of attack between air and the airfoil. For example, and without limitation, electric aircraft600are designed to be as lightweight as possible. Reducing the weight of the aircraft and designing to reduce the number of components is essential to optimize the weight. To save energy, it may be useful to reduce weight of components of an electric aircraft600, including without limitation propulsors and/or propulsion assemblies. In an embodiment, the motor may eliminate need for many external structural features that otherwise might be needed to join one component to another component. The motor may also increase energy efficiency by enabling a lower physical propulsor profile, reducing drag and/or wind resistance. This may also increase durability by lessening the extent to which drag and/or wind resistance add to forces acting on electric aircraft600and/or propulsors.

Still referring toFIG.6, electric aircraft600may include at least a sensor104coupled to the electric aircraft. In one embodiment, electric aircraft600may include a flight controller108, where the flight controller may be configured to operate the electric aircraft as a function of the data transmitted by the at least a sensor104coupled to the aircraft.

Computer system700may also include a storage device724. Examples of a storage device (e.g., storage device724) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device724may be connected to bus712by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device724(or one or more components thereof) may be removably interfaced with computer system700(e.g., via an external port connector (not shown)). Particularly, storage device724and an associated machine-readable medium728may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system700. In one example, software720may reside, completely or partially, within machine-readable medium728. In another example, software720may reside, completely or partially, within processor704.

Computer system700may also include an input device732. In one example, a user of computer system700may enter commands and/or other information into computer system700via input device732. Examples of an input device732include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device732may be interfaced to bus712via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus712, and any combinations thereof. Input device732may include a touch screen interface that may be a part of or separate from display736, discussed further below. Input device732may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system700via storage device724(e.g., a removable disk drive, a flash drive, etc.) and/or network interface device740. A network interface device, such as network interface device740, may be utilized for connecting computer system700to one or more of a variety of networks, such as network744, and one or more remote devices748connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network744, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software720, etc.) may be communicated to and/or from computer system700via network interface device740.