Patent ID: 12205340

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG.1shows a tensor (t) the tensor is characterized by elements (e), which are organized along a height (h), width (w), and depth (d) of the tensor. The dimension of the tensor (t) corresponding to the height (w) may also be understood as a vertical axis of the tensor (t) while the dimension of the tensor (t) corresponding to the width (w) may be understood as a horizontal axis of the tensor (t). The matrix slices along the depth (d) dimension of the tensor (t) may also be understood as channels of the tensor (t). The tensor (t) may, for example, characterize an image, e.g., an RGB image. In this case, each channel characterizes a color channel of the image and each element (e) characterizes a pixel value of the respective red, green or blue channel. The image may also be given as a gray scale image, in which case the tensor (t) only comprises a single channel and each element (e) characterizes the intensity of a pixel. The tensor (t) may also characterize feature vectors. Each feature vector comprises elements (e) along the depth dimension of the tensor (t). In addition, each feature vector is characterized by a position along the height (h) and width (w) of the tensor.

FIG.2schematically shows operations of a 1D-convolutional layer (61). The 1D-convolutional layer accepts an input (i), which is preferably be given in the form of a tensor (t). The input (i) is provided to a first filter (c1) of the 1D-convolutional layer (61). The first filter (c1) comprises a trainable weight for each element (e) in a slice along the depth (d) dimension of the input (i). The first filter (c1) processes the input (i) by performing a discrete convolution of the first filter (c1) with the input (i).FIG.2shows an embodiment, in which the first filter (c1) operates along the height (h) dimension of the input (i). In other embodiments (not shown), the first filter (c1) may also operate along the width (w) of the input (i). A first output (o1) if the first filter (c1) may then be provided as output (o) of the 1D-convolutional layer (61). Alternatively, it is also possible that the 1D-convolutional layer (61) comprises a plurality of filters (c1, c2, c3), each comprising a trainable weight for each element (e) in a slice along the depth (d) dimension of the input (i). Each filter of the plurality of filters (c1, c2, c3) may then process the input (i) individually and output a respective output (o1, o2, o3). This way, a plurality of outputs from the filters (c1, c2, c3) may be obtained, which in turn may be concatenated along a predefined dimension in order to determine the output (o) of the 1D-convolutional layer (62).

FIG.3shows a preferred method concerning how to extract feature vectors from an image (S) and aggregating the extracted feature vectors into a tensor (x) that may be used as input for a neural network. The image (S) is split into disjoint patches (p). For each image, a feature vector is extracted. The feature vector may, for example, be a histogram of oriented gradients (g). The histogram may then be cast into a vector, which is used as feature vector of the tensor (x).

FIG.4shows a neural network (60) comprising two 1D-convolutional layers (61,62). The neural network (60) comprises a plurality of convolutional layers (C1, C2, Cn), wherein the plurality of convolutional layers (C1, C2, C_n) may be configured to accept an input signal (x) as input and provide a tensor (t) characterizing a feature representation (f) of the input signal (x). The feature representation (f) is then provided to a first 1D-convolutional layer (61) and a second 1D-convolutional layer (62). The first 1D-convolutional layer (61) is configured to operate along the height (h) dimension of the feature representation (f) while the second 1D-convolutional layer is configured to operate along the width (w) dimension of the feature representation (f). By virtue of being obtained from a convolutional layer, the feature representation (f) may also be understood as convolution output. The first 1D-convolutional layer (61) and the second 1D-convolutional layer (62) determine a first convolution output (co1) and a second convolution output respectively (co2). The first convolutional output (co1) and the second convolutional output (co2) may especially each be a matrix. The first convolution output (co1) and the second convolution output (co2) may then be provided as input to a fully connected layer (F1) of the neural network (60). In order to be used as input, the first convolution output (co1) and the second convolution output (co2) may each be flattened to a vector and the resulting vectors may then be concatenated to form a larger vector that is used as input to the fully connected layer (F1). The fully connected layer (F1) may especially be part of a plurality of fully connected layers (F1, F2, F3), i.e., be part of a multilayer perceptron that is part of the neural network (60). An output of the multilayer perceptron may then be provided as output signal (y), i.e., as output of the neural network (60). If the fully connected layer (F1) is used for providing an output of the neural network (60), the output of the fully connected layer (F1) may be provided as output signal (y.)

In further embodiments, it is also possible that the input signal (x) comprises feature vectors, wherein each feature vector characterizes a patch of an image. A patch of the image may be understood as a plurality of pixels of the image, wherein each patch characterizes a rectangular region of the image and the image can be split into disjoint patches. The feature vectors may especially characterize SIFT, SURF, SWIFT or other gradient features of the patches of the image.

The neural network (60) may especially be trained to determine based on the input signal (x) whether an optical sensor from which the input signal (x) was determined is obstructed or not. The output signal (y) may especially characterize a probability value characterizing the probability of the optical sensor being obstructed. Additionally or alternatively, the output signal (y) may also characterize the probability of the optical sensor to not be obstructed.

The neural network (60) may especially be trained in a supervised fashion, preferably by means of a (potentially stochastic) gradient descent algorithm or an evolutionary algorithm. For training, the neural network (60) may be provided a training input signal and a desired output signal characterizing whether the training input signal characterizes an obstructed optical sensor or not.

FIG.5shows an embodiment of an actuator (10) in its environment (20). The actuator (10) interacts with a control system (40). The actuator (10) and its environment (20) will be jointly called actuator system. At preferably evenly spaced points in time, an optical sensor (30) senses a condition of the actuator system. The optical sensor (30) may comprise several sensors. An output signal (S) of optical the sensor (30) (or, in case the sensor (30) comprises a plurality of sensors, an output signal (S) for each of the sensors) which encodes the sensed condition is transmitted to the control system (40).

Thereby, the control system (40) receives a stream of sensor signals (S). It then computes a series of control signals (A) depending on the stream of sensor signals (S), which are then transmitted to the actuator (10).

The control system (40) receives the stream of sensor signals (S) of the sensor (30) in an optional receiving unit (50). The receiving unit (50) transforms the sensor signals (S) into input signals (x). Alternatively, in case of no receiving unit (50), each sensor signal (S) may directly be taken as an input signal (x). The input signal (x) may, for example, be given as an excerpt from the sensor signal (S). Alternatively, the sensor signal (S) may be processed to yield the input signal (x), e.g., by extracting feature vectors for patches of the sensor signals (S). The input signal (x) may also characterize feature vectors that have been filtered. For example the receiving unit (50) may save feature vectors for other sensor signals preceding the sensor signal (S) and may then determine median feature vectors or average feature vectors for the sensor signals and the other sensor signal (S). In other words, the input signal (x) is provided in accordance with the sensor signal (S).

The input signal (x) is then passed on to the neural network (60).

The neural network (60) is parametrized by parameters (Φ), which are stored in and provided by a parameter storage (St1).

The neural network (60) determines an output signal (y) from the input signals (x). The output signal (y) is transmitted to an optional conversion unit (80), which converts the output signal (y) into the control signals (A). The control signals (A) are then transmitted to the actuator (10) for controlling the actuator (10) accordingly. Alternatively, the output signal (y) may directly be taken as control signal (A).

The actuator (10) receives control signals (A), is controlled accordingly and carries out an action corresponding to the control signal (A). The actuator (10) may comprise a control logic which transforms the control signal (A) into a further control signal, which is then used to control actuator (10).

In further embodiments, the control system (40) may comprise the sensor (30). In even further embodiments, the control system (40) alternatively or additionally may comprise an actuator (10).

In still further embodiments, it can be envisioned that the control system (40) controls a display (10a) instead of or in addition to the actuator (10).

Furthermore, the control system (40) may comprise at least one processor (45) and at least one machine-readable storage medium (46) on which instructions are stored which, if carried out, cause the control system (40) to carry out a method according to an aspect of the invention.

FIG.6shows an embodiment in which the control system (40) is used to control an at least partially autonomous robot, e.g., an at least partially autonomous vehicle (100).

The optical sensor (30) may comprise one or more video sensors and/or one or more radar sensors and/or one or more ultrasonic sensors and/or one or more LiDAR sensors. Some or all of these sensors are preferably but not necessarily integrated in the vehicle (100).

The neural network (60) may be configured to detect obstructions of an optical sensor (30) of the vehicle (100). The control signal (A) may then be chosen in accordance with the output signal (y) determined from the neural network (60). For example, if the optical sensor (30) is obstructed, a detection of the environment (20) of the vehicle (100) may be obtained without considering information from the obstructed optical sensor (30). For example, the vehicle (100) may be equipped with a camera sensor and a LIDAR sensor, which are both used for detecting objects in the vicinity of the vehicle (100). The neural network (60) may be configured to determine if the camera sensor is obstructed or not. If it is classified as obstructed, objects detected based on images of the camera sensor or a detection of no objects based on images of the camera sensor may be ignored for determining routes of the vehicle (100) and/or navigating the vehicle (100).

Alternatively, for example when using only a camera sensor for determining the environment (20) of the vehicle (100), it is also possible to hand over operation of the vehicle (100) to a human driver or operator in case it is determined that an optical sensor (30) of the vehicle (100) is obstructed.

The actuator (10), which is preferably integrated in the vehicle (100), may be given by a brake, a propulsion system, an engine, a drivetrain, or a steering of the vehicle (100). The control signal (A) may be determined such that the actuator (10) is controlled such that vehicle (100) avoids collisions with objects in the vicinity of the vehicle (100).

Alternatively or additionally, the control signal (A) may also be used to control the display (10a), e.g., for displaying to a driver or operator of the vehicle (100) that the optical sensor (30) is obstructed. It can also be imagined that the control signal (A) may control the display (10a) such that it produces a warning signal, if the optical sensor (30) is classified to be obstructed. The warning signal may be a warning sound and/or a haptic signal, e.g., a vibration of a steering wheel of the vehicle (100).

In further embodiments, the at least partially autonomous robot may be given by another mobile robot (not shown), which may, for example, move by flying, swimming, diving or stepping. The mobile robot may, inter alia, be an at least partially autonomous lawn mower, or an at least partially autonomous cleaning robot. In all of the above embodiments, the control signal (A) may be determined such that propulsion unit and/or steering and/or brake of the mobile robot are controlled such that the mobile robot may avoid collisions with said identified objects.

In a further embodiment, the at least partially autonomous robot may be given by a gardening robot (not shown), which uses the optical sensor (30) to determine a state of plants in the environment (20). The actuator (10) may control a nozzle for spraying liquids and/or a cutting device, e.g., a blade. Depending on an identified species and/or an identified state of the plants, a control signal (A) may be determined to cause the actuator (10) to spray the plants with a suitable quantity of suitable liquids and/or cut the plants.

In even further embodiments, the at least partially autonomous robot may be given by a domestic appliance (not shown), like e.g. a washing machine, a stove, an oven, a microwave, or a dishwasher. The optical sensor (30) may detect a state of an object which is to undergo processing by the household appliance. For example, in the case of the domestic appliance being a washing machine, the optical sensor (30) may detect a state of the laundry inside the washing machine. The control signal (A) may then be determined depending on a detected material of the laundry.

FIG.7shows an embodiment in which the control system (40) is used to control a manufacturing machine (11), e.g., a punch cutter, a cutter, a gun drill or a gripper, of a manufacturing system (200), e.g., as part of a production line. The manufacturing machine may comprise a transportation device, e.g., a conveyer belt or an assembly line, which moves a manufactured product (12). The control system (40) controls an actuator (10), which in turn controls the manufacturing machine (11).

The optical sensor (30) may capture properties of, e.g., a manufactured product (12). The actuator (10) may be controlled depending on a position detected by, e.g., a second neural network. For example, the actuator (10) may be controlled to cut the manufactured product at a specific location of the manufactured product itself. Alternatively, it may be envisioned that the second neural network classifies, whether the manufactured product is broken or exhibits a defect. The actuator (10) may then be controlled as to remove the manufactured product from the transportation device.

The neural network (60) may be configured to whether the optical sensor (30) is obstructed or not. If the optical sensor (30) is classified as obstructed, the manufacturing machine (11) may be stopped and/or an operator or technician may be alerted to conduct maintenance on the manufacturing machine (11).

The term “computer” may be understood as covering any devices for the processing of pre-defined calculation rules. These calculation rules can be in the form of software, hardware or a mixture of software and hardware.

In general, a plurality can be understood to be indexed, that is, each element of the plurality is assigned a unique index, preferably by assigning consecutive integers to the elements contained in the plurality. Preferably, if a plurality comprises N elements, wherein N is the number of elements in the plurality, the elements are assigned the integers from 1 to N. It may also be understood that elements of the plurality can be accessed by their index.