Reviewerly/exHarmony huggingface dataset:
Energy-Minimization Path Planning and Control of Unmanned Aerial Systems for Advanced Air Mobility

Battery limitations continue to be a bottleneck for mass-adoption of electric drones within logistics applications, such as short-haul parcel or medical supply delivery. Therefore, it is becoming progressively more important to utilize the available energy in a most efficient manner. Energy optimization research provides us with increasingly accurate models for energy consumption estimation during drone flight, but there is scant research pertaining to real-time trajectory adjustment to ensure minimum energy usage. Additionally, few governing models take into account energy demands due to landing and take-off. A novel approach is introduced that uses a PID controller combined with fundamental energy equations that tracks real-time energy optimization along a predetermined path under parameter uncertainty. It is demonstrated that the controller effectively and accurately converges to the most optimal velocity with respect to the energy per meter (EPM) metric. Additionally, path optimization using Model Predictive Control is introduced within landing and take-off stages to further enhance the effectiveness of the proposed approach.
Uncertainty-based Sensor Fusion Architecture using Bayesian-LSTM Neural Network

Uncertainty-based sensor management for positioning is an essential component in safe drone operations inside urban environments with large urban valleys. These canyons significantly restrict the Line-Of-Sight signal conditions required for accurate positioning using Global Navigation Satellite Systems (GNSS). Therefore, sensor fusion solutions need to be in place which can take advantage of alternative Positioning, Navigation and Timing (PNT) sensors such as accelerometers or gyroscopes to complement GNSS information. Recent state-of-art research has focused on Machine Learning (ML) techniques such as Support Vector Machines (SVM) that utilize statistical learning to provide an output for a given input. However, understanding the uncertainty of these predictions made by Deep Learning (DL) models can help improve integrity of fusion systems. Therefore, there is a need for a DL model that can also provide uncertainty-related information as the output. This paper proposes a Bayesian-LSTM Neural Network (BLSTMNN) that is used to fuse GNSS and Inertial Measurement Unit (IMU) data. Furthermore, Protection Level (PL) is estimated based on the uncertainty distribution given by the system. To test the algorithm, Hardware-In-the-Loop (HIL) simulation has been performed, utilizing Spirent's GSS7000 simulator and OKTAL-SE Sim3D to simulate GNSS propagation in urban canyons. SimSENSOR is used to simulate the accelerometer and gyroscope. Results show that Bayesian-LSTM provides the best fusion performance compared to GNSS alone, and GNSS/IMU fusion using EKF and SVM. Furthermore, regarding uncertainty estimates, the proposed algorithm can estimate the positioning boundaries correctly, with an error rate of 0.4% and with an accuracy of 99.6%.
GlareNet: A Deep Learning Approach To Removing Glare From Images Taken in Proximity Operations

Optical sensors have been used aboard satellites for a variety of purposes. In the case of proximity operations in low Earth orbit, these sensors may be used to capture images of a target satellite from a chaser. From these images state information may be obtained about the target. However, due to adverse lighting conditions caused by glare from the Sun, the images that are input to the algorithms responsible for computing state information may produce erroneous results. We propose GlareNet, a deep learning based solution to this problem. In recent years Generative Adversarial Networks (GAN) have been making strides in harnessing neural networks to generate images, and alter images to remove possible unwanted qualities, such as blur. GlareNet utilizes a conditional deep convolutional GAN (DCGAN) in order to receive an image where there is significant glare around a target satellite and subsequently remove the glare while retaining the structure of the satellite even in cases where the glare occludes parts of the target vehicle.
Reinforcement Learning for Pan-Tilt-Zoom Camera Control, with Focus on Drone Tracking

Reliable detection and tracking of objects using pan-tilt-zoom (PTZ) cameras is an unsolved problem. We attempt to answer whether the use of reinforcement learning (RL) is an appropriate tool for solving it. We present an environment for training RL agents to track a drone using a (PTZ) camera. We also present an agent trained using this environment, which learns to correctly pan, tilt, and zoom the camera to follow a randomly moving drone, using continuous actions. The input into the agent is the RGB image observed by the camera. The agent is rewarded for correctly tracking the drone, and penalized if it loses it from its viewport. We use the recurrent proximal policy optimization (PPO) algorithm with a long short-term memory (LSTM) layer. We find that the agent reliably learns ways of tracking the drone after around 1.4 million steps of training.
An iterative scheme to learn system dynamics of space objects from partial state information

Improved computational infrastructure and abundant data have made studying physical systems comparatively easy in recent years. Data-Driven techniques based on system identification and machine learning are leading efforts in this domain. However, the ability to learn from partial measurements, owing to various reasons such as corrupted/unusable data or just the inability to capture certain types of information due to lack of sensors is emerging to be a central challenge. Restricted observational capabilities and lack of robust sensors within the aerospace domain, specifically resident space object observation, requires alternative compensation techniques. Currently, range measurements based on radar/optics are most commonly used for orbit determination as true state information from ephemeris data is almost exclusively reserved for satellite operators. This paper aims to develop a technique that can reconstruct the complete dynamics of a space object's dynamics from low-fidelity incomplete state measurements. Methods developed are applied to different types of measurements; generated from mathematical models, obtained from satellite operators, and noisy models replicating sensor observations. This work proposes an iterative learning scheme inspired by the value function iteration framework for solving Hamilton–Jacobi–Bellman (HJB) equations. % The method of time-delay embedding justified by Taken's theorem has demonstrated with reasonable success the reconstruction of low dimensional, non-linear systems purely from scalar time series. An imperfect version of full-state data is created by using suitable initial guesses for the missing states. The guesses and the estimates of the dynamics are then iteratively improved within this scheme. It can be split into a two-step process with a nested loop structure, an inner and an outer loop. The outer loop will function as a state updater and will provide new guesses as input to each iteration. The inner loop is a data-driven technique that can learn the spatio-temporal evolution of a physical system from data. This work considers i) a Hankel-based DMD (Dynamic Mode Decomposition) method, ii) a Feed-forward neural network, and iii) an LSTM(Long Short-Term Memory) network as suitable candidates for the inner loop. This paper presents initial ideas, methods, and observations.
Reinforcement learning based Linear quadratic Regulator for the Control of a Quadcopter

In a practical implementation of the Linear Quadratic Regulator (LQR) for the control of a quadrotor drone, a key problem is the choice of the state and control weighting matrices. In this paper, we propose a Reinforcement Learning based LQR for quadrotor control. Leveraging the advances in deep reinforcement learning, Deep Deterministic Policy Gradient (DDPG) model is used to reset the elements of the Q matrix to achieve a faster response while minimizing the integral square error (ISE). Following the properties of the LQR control law, the LQR-DDPG controller is optimal and asymptotically stable. The proposed controller is compared with four other extensively used methods to choose the Q matrix. In the first method, that Q matrix is initially selected to be an identity matrix. Bryson’s rule is used to set the Q matrix in the second method but not updated subsequently. Similar to the second method, the third method uses Bryson’s rule to set the Q matrix but uses a proportional derivative controller along with LQR. The third method uses an iterative optimization algorithm that minimizes the integral square error (ISE) over the training trajectories to select the Q matrix. The simulation results show that LQR-DDPG is better than all benchmarking cases in terms of rise time, settling time and time of flight, all by a margin of 10% or more.
Design of an All-Terrain Aerial Robotic Interface (ATARI) as a Collaborative Platform for UAVs

Autonomous missions typically involve multimodal aspects in which teaming air and ground vehicles with differing capabilities can increase the efficiency of operation. Such collaboration can accommodate more complex missions by better allocating each vehicles’ resources to complete complex tasks. This research will investigate the benefits of combining the strengths of the air and ground domains to extend the task capabilities and improve the efficiency of operations. Specifically, an Unmanned Ground Vehicle will serve as a mobile, leveling, landing platform for an Unmanned Aerial Vehicle, allowing it to takeoff and land on uneven terrain, and increase useful flight time. Future topics of interest for the system include swappable payloads, solar charging, and adding manipulators. This paper focuses on the design of the combined air-ground system, the systems engineering of the project, and current construction of the prototype vehicles.
The Impact of Digital Finance on the Commercial Circulation Industry Based on IoT and Big Data

Digital finance represented by the Internet of Things (IoT) has strong penetration and integration. The real economy has always been an important cornerstone of social and economic development. As a bridge between producers and consumers, the commercial circulation industry also plays an irreplaceable role in the healthy development of social economy. Therefore, this study is based on the Capability Maturity Model (CMM), the integration maturity is divided into four categories, and the results are qualitatively analyzed. The research results show that under the Internet of Things mode, the integration development maturity level and the integration development maturity are concentrated at a lower stage (defined level).
Real-Time Path Optimization for 3D UAS Line Survey Operations

In this work, an autonomy package is developed that can be used to optimize a flight trajectory in real-time through a series of 3-Dimensional survey lines. These trajectories are generated by extending standard 2-Dimensional Dubins Paths, which take into account the turn radius constraints of the Unmanned Aerial System, with the unique flight altitudes of the survey lines, while constraining the path based on climb and descent rate limitations. In order to generate an optimal path through the series of survey lines, Ant Colony Optimization is used in order to solve the Traveling Salesman Problem, using the Dubins Path lengths between respective survey lines to populate the cost matrices. Longitudinal and Lateral-Directional aircraft guidance methods are implemented in order to track the desired trajectory. The autonomy package is first tested in a simulation environment, where the resulting routes and tracking performance is assessed for an example mission scenario. Finally, the autonomy package is integrated onto an embedded system, which is used to control the aircraft during preliminary flight test validation, and the resulting autonomous behaviors and tracking performance is assessed.
Solar Power Forecasting in Photovoltaic Modules Using Machine Learning

As fossil fuels become increasingly scarce, the globe seeks a dependable, clean, and pollution-free energy source, thus solar power is gaining traction. This makes the analysis of solar power to be generated highly important. This chapter analyses various time series methods like seasonal auto-regressive integrated moving average with exogenous factors (SARIMAX), auto-regressive integrated moving average (ARIMA), Holt-Winters and auto-regression (AR) to forecast solar power generated in household solar panels in order to determine which method can estimate the value of photovoltaic power accurately. After applying various pre-processing techniques, it is determined that Holt-Winters method for time series forecasting in additive mode predicts the values closest to the actual values of the solar power with a root mean squared error (RMSE) score of 5.3949.
Safe Optimal Control with Synthesized Waypoints as Guidance

This paper proposes a safe and optimal control framework using waypoints and associated reference trajectories as guidance. The waypoints are obtained by efficiently solving satisfiability problems over linear constraints. The waypoint synthesis formulation can incorporate control errors and ensure that the computed reference trajectory still satisfies given specifications even in the presence of error. We further use the optimal control framework with an augmented tracking-error-minimization objective to closely follow the reference trajectory. Since the reference trajectory is safe by design, the ideal tracking performance of the reference also implies the safety of the closed-loop state trajectory, which eliminates the need for additional safety constraints that may hinder solution optimality. We compute the optimal control law for stochastic systems using a path-integral reformulation. Our approach outperforms the optimal control framework without a synthesized reference trajectory in terms of trajectory optimality and goal-reach accuracy, as validated by numerical simulations on a UAV model passing through an obstacle-cluttered environment.
Data-Driven and Robust Path-following Control of a Quadrotor Slung Load Transport System

In this paper, a robust path following control law for a quadrotor Slung Load Transport System is developed. A Gaussian Process-augmented Extended Kalman Filter is proposed to estimate payload states. In this approach, Gaussian Processes are used to compensate for unmodelled dynamics in the process model, and they are trained on previously collected data of a Slung Load Transport System in flight. Both simulations and experiments verify the estimation and control system framework and demonstrate successful stabilization and trajectory tracking of the Slung Load Transport System, overcoming model inaccuracy and disturbances.
Reward Function Design for Stand-Off Tracking of Reinforcement Learning

As a reward function encodes the objective of reinforcement learning, the overall control performance and convergence rate of the learning can be adjusted by reward function design. In this study, new reward functions are proposed for control using reinforcement learning. With an attempt to utilize Lyapunov stability theory, the proposed reward functions are needed to set Lyapunov candidate function. The reward is computed depending on whether the variation of the Lyapunov candidate function is negative or positive. As a result, the reinforcement learning agent learns which region of state is good or bad. With exponential function whose exponent is a quadratic function, the agent is also guided to learn how to get the good region. The stability of the proposed reward functions is analyzed. To verify the performance of the reward function, numerical simulation is performed for stand-off tracking using Proximal Policy Optimization algorithm. The results show that the proposed reward functions have improved regulation performance and data efficiency.
Circular Flight-Path Optimization for a Solar-Powered UAV Flying in Horizontal Winds

The loiter radius with the highest chargerate for a solar-powered aircraft flying circuits over a fixed point on the ground is determined. The aircraft is assumed to be flying in horizontal atmospheric winds. Chargerate is calculated as the difference between the average solar-power input and power required to fly the circular loiter for each wind scenario. Model validation was performed using flight test data from the CREATeV solar-powered aircraft, with comparisons found to be within a 10% difference. A parametric sweep was performed to determine the optimum loiter at different sun elevations for various wind conditions. It was found that, for the solar-powered aircraft under consideration, in most scenarios, the chargerate is maximized by flying a large loiter radius. The exceptions to this case are when: (1) The sun elevation is higher than 20 degrees, and there is a high windspeed with a large component of the wind vector perpendicular to the sun vector, and (2) The sun elevation is lower than 20 degrees, and the wind vector is not aligned with the sun vector. Overall, flying with a changing loiter radius based on wind conditions and sun position can lead to a power gain of up to 11 W. Although these results are specific to the CREATeV solar-powered aircraft, the model discussed in this paper can be modified to apply to any solar-powered aircraft.
Real-Time Motion Planning for Quadcopter Using Adaptive Spherical Expansion and Sequential Convex Optimization

This paper introduces the Adaptive Spherical Expansion and Sequential Convex Programming (ASE-SCP) as a real-time motion planning algorithm. ASE-SCP algorithm is an improved version of the Spherical Expansion and Sequential Convex Programming (SE-SCP) algorithm in terms of computational speed. ASE-SCP is a hybrid real-time motion planning algorithm which combines the advantages of the Adaptive Spherical Expansion, such as approaching the neighborhood of the global optimal path, and the quick convergence ability of the Sequential Convex Programming. The ASE-SCP algorithm first finds a collision-free path using the adaptive spherical expansion approach. After finding a feasible path from the start point to the target point, the feasible path is re-optimized (tuned) using sequential convex optimization to find the suboptimal path. ASE-SCP Algorithm is applied to a quadcopter model to demonstrate its applicability.
Design, Modeling, Simulation, and Flight Testing of a Custom-Built Tail-Sitter UAV

This paper presents the design, manufacturing, and flight-testing of a low-cost autonomous dual motor tail-sitter UAV for the search and rescue of drowning victims. A systems engineering approach was used to meet certain design requirements. Trade studies were conducted for the selection of materials and hardware component. XFLR5 aerodynamic analysis software was used to iteratively design the wing planform, optimize center of gravity, and to assess static and dynamic stability. Simulations of the vehicle dynamics and control were performed using RealFlight 9.5, Mission Planner, and ArduPilot software. The simulation environment was used also to tune the controller gains for stabilization of unstable modes. Multiple vehicles were manufactured and two of them were used for flight testing. Simulations were found to be extremely useful and effective to train both the operator and the flight controller. Simulation and flight test results are shown.
Learning Stochastic Processes Using Gaussian Processes: An Application to Flight Delay Prediction

This paper presents a machine learning technique to predict delays and, based on this forecast, plan flight operations and advisories. Neural networks are also widely researched for predicting flight delays, but these deep learning techniques are mathematically complex. This research adopts Gaussian processes as a supervised learning technique to predict flight delays. It is a data-driven machine learning technique that trains a model with prior knowledge, i.e., mean and covariance functions associated with known data. To predict delays, the model is trained with features like flight day, day of the week, month, departure time, etc. Flights from different routes are considered for prediction, and the efficacy of the presented learning technique is studied by comparison with actual delays of the flights. As it is impossible to predict delay time accurately, we instead predict the delays showing a 95% confidence interval.
A Formal Approach to Engineering AI-based Systems: An Introduction

For traditional (non-AI) large-scale complex engineered systems, the problem space is defined using textual needs statements, and requirements, where problem space refers to the space of outcomes that are desired from the solution. Although it is easier to identify and represent needs and requirements, the textual representation significantly lacks in facilitating any analysis/reasoning capabilities due to the lack of two qualities - precision and correctness. Given that data-centric AI-based systems are non-behavior-preserving, it becomes even more challenging to use textual representations to define requirements/specifications to design these systems. This paper focuses on highlighting the challenges that make it difficult to formulate the correct problem space for AI-based systems. In addition, this paper highlights how Modal Preference Logic, augmented with Decision Analysis, can serve as a potential solution to overcome some of the challenges in engineering these systems.
Drone Navigation in Unreal Engine Using Generative Adversarial Imitation Learning

Learning from human demonstrations is fundamental to harnessing human intelligence in many tasks. A critical approach to learning from human demonstrations is inverse reinforcement learning, which aims to learn rewards from limited human demonstrations and then train control policies based on the learned reward. The existing inverse reinforcement learning methods perform well in less complex environments but often fail in complex high-dimensional environments. To overcome these difficulties and limitations, this paper studies the implementation of a generative adversarial imitation learning (GAIL) method that controls a quadcopter Unmanned Aerial Vehicle (UAV) to navigate between two defined positions in a virtual environment created in Unreal Engine, whose simulation environments can reflect real-world physics. We present procedures to build a customized virtual environment using the Epic game's {Unreal engine}, collect expert demonstrations, and optimize the control policy using GAIL. Finally, the simulation results discuss and explain the performance of GAIL in 3-dimensional UAV navigation.