Abstract

In this study, we address the challenge of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) through an innovative composite imitation learning ap- proach that combines Proximal Policy Optimization (PPO) with Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL), enriched by the integration of ray-tracing techniques. Our research underscores the sig- nificant role of ray-tracing in enhancing obstacle detection and avoidance capabilities. Moreover, we demonstrate the effectiveness of incorporating GAIL in coordinating the flight paths of two UAVs, showcasing improved collision avoidance capabilities. Extending our methodology, we apply our combined PPO, BC, GAIL, and ray-tracing framework to scenarios involving four UAVs, illustrating its scalability and adaptability to more complex scenarios. The findings indicate that our approach not only improves the reliability of basic PPO based obstacle avoidance but also paves the way for advanced autonomous UAV operations in crowded or dynamic environments.

Abstract

This research paper presents a novel low computational approach for navigating a micro UAV (Unmanned Aerial Vehicle) through narrow passages using only its onboard camera feed and a PID control system. The proposed method uses edge detection and homography techniques to extract the key features of the passage from the camera feed, and then employs a tuned PID controller to guide the UAV through and out of the passage while avoiding collisions with the walls. To evaluate the effectiveness of the proposed approach, a series of experiments were conducted using a micro-UAV navigating in and out of a custom-built test environment (constrained rectangular box). The results demonstrate that the system is able to successfully guide the UAV through the passages while avoiding collisions with the walls

Abstract

This paper introduces a novel approach for fault detection and localization in a motor of a Hexacopter UAV. The proposed two-stage architecture leverages Long Short-Term Memory (LSTM) networks for latent temporal feature extraction and Random Forest for localization. By combining them, we see improved fault detection and isolation performance. Our evaluations show the robustness of this approach in varying noise levels and real-world-like environments. Analysis of computational efficiency in rapid detection shows that the model identified faults within 2-5 time steps of the flight. Finally, we show that the proposed method surpasses classical statistical models and deep learning techniques in terms of overall accuracy (96.78%).

Abstract

This paper presents a novel algorithm for a swarm of unmanned aerial vehicles to search for an unknown source. The proposed method is inspired by the well-known particle swarm optimization (PSO) algorithm and is called accelerationbased particle swarm optimization (APSO) to address the sourceseeking problem with no a priori information. Unlike the conventional particle swarm optimization algorithm, where the particle velocity is updated based on the self-cognition and socialcognition information, here the update is performed on the particle acceleration. A theoretical analysis is provided, showing the stability and convergence of the proposed acceleration-based particle swarm optimization algorithm. Conditions on the parameters of the resulting third-order update equations are obtained using Jury’s stability test. High-fidelity simulations performed in CoppeliaSim, show the improved performance of the proposed acceleration-based particle swarm optimization algorithm for searching an unknown source when compared with the state-ofthe-art particle swarm-based source-seeking algorithms. From the obtained results, it is observed that the proposed method performs better than the existing methods under scenarios like different inter unmanned aerial vehicle communication network topologies, varying numbers of unmanned aerial vehicles in the swarm, different sizes of search regions, restricted source movement, and in the presence of measurements noise.

Abstract

Deep Reinforcement Learning is quickly becoming a popular method for training autonomous Unmanned Aerial Vehicles (UAVs). Our work analyzes the effects of measurement uncertainty on the performance of Deep Reinforcement Learning (DRL) based waypoint navigation and obstacle avoidance for UAVs. Measurement uncertainty originates from noise in the sensors used for localization and detecting obstacles. Measurement uncertainty/noise is considered to follow a Gaussian probability distribution with unknown non-zero mean and variance. We evaluate the performance of a DRL agent, trained using the Proximal Policy Optimization (PPO) algorithm in an environment with continuous state and action spaces. The environment is randomized with different numbers of obstacles for each simulation episode in the presence of varying degrees of noise, to capture the effects of realistic sensor measurements. Denoising techniques like the low pass filter and Kalman filter improve performance in the presence of unbiased noise. Moreover, we show that artificially injecting noise into the measurements during evaluation actually improves performance in certain scenarios. Extensive training and testing of the DRL agent under various UAV navigation scenarios are performed in the PyBullet physics simulator. To evaluate the practical validity of our method, we port the policy trained in simulation onto a real UAV without any further modifications and verify the results in a real-world environment.

Abstract

The control system design specifications for dynamical systems are typically provided in terms of the desired transient response and steady-state response. Meeting such requirements for dynamical systems whose mathematical models are unavailable is a challenging task. In this paper, we propose a learning-based controller to achieve the desired control system design specifications for unknown dynamical systems. We consider a SoTA model-free reinforcement learning agent (TD3) in the continuous state and action space setting where the agent has no knowledge of system dynamics. The selection of an appropriate reward function is a key factor that shapes the response of the dynamical system when the learning-based controller is implemented. The resulting controller is trained and tested for first-order and second-order linear systems, as well as a nonlinear system. The resulting trajectories of the closed-loop systems indicate that the transient and steady-state response can be altered by choosing the appropriate reward function while adhering to the constraints imposed on the control input.

Abstract

Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component’s contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building

Abstract

This paper discusses the development of autopilot hardware for small fixed-wing air vehicles. Weight constraint is the critical factor in developing such hardware. The sensors and communication devices are selected based on the requirements and constraints of these air vehicles. The sensors used in the hardware are calibrated using a three-axis rotating platform. The software written in the autopilot hardware is flexible enough to incorporate complex estimation and control algorithms along with the hardware-in-loop simulations. Linear output feedback controllers are designed for fixed wing micro and nano air vehicles. Successful flight trials are conducted to demonstrate the utility of the autopilot hardware for small fixed-wing air vehicles

Abstract

In this paper, we propose a decentralized learning algorithm to restore communication connectivity during multi-agent formation control. The time-varying connectivity profile of a mobile multi-agent system represents the dynamic information exchange capabilities among agents. While connected to the neighbors, each mobile agent in the proposed scheme learns to raise the team connectivity. When the inter-agent communication is lost, the associated trained neural network generates appropriate control actions to restore connectivity. The proposed learning technique leverages an adaptive control formalism, wherein a neural network tries to mimic the negative gradient of a value that relies on the agent-to-neighbor distances. All agents use the conventional consensus protocol during the connected multi-agent dynamics, and under communication loss, only the lost agent executes the neural network predicted actions to come back to the fleet. Simulation results demonstrate the effectiveness of our proposed approach for single/multiple agent loss even in the presence of velocity disturbances.

Abstract

Kalman filter (KF) and its variants are commonly used in estimating states of dynamical systems. For accurate estimation of states, researchers have developed intelligent KFs by combining them with machine learning algorithms. The inherent variations in the vehicle dynamics and uncertainties in the environment require the development of an autonomous sequential framework for continuous estimation. Further, the need for real-time estimation of states in certain applications requires low computer memory usage and low computational cost while implementing autonomous-structured learning algorithms. In this paper, a parsimonious autonomous sequential estimator (PASE) is proposed, which combines the KF-based estimator and autonomous-structured recurrent parsimonious learning machine (rPALM) in a sequential manner. The rPALM overcomes the dependency on target variables while point-to-hyperplane distance calculation. The performance of PASE has been evaluated extensively by comparing it with various batch-learning algorithms and single-pass learning-based intelligent estimators. The results clearly indicate that PASE provides better estimation accuracy with a compact architecture for both linear and nonlinear dynamical systems. Finally, the performance of PASE has been evaluated with experimental data for the state estimation of an unmanned ground vehicle while the training of the learning machine is performed with the simulated data. The estimation accuracy in such a scenario is justifying its appropriateness in real-