Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Distributed time-critical coordination strategies for unmanned aerial systems in cluttered environments

This thesis addresses the problem of cooperative motion planning and control for a group of cooperating unmanned aerial systems through cluttered and uncertain environments, subject to a broad range of coordination and temporal constraints. The proposed solution expands the type of time-critical missions that can be automated using cooperative motion control frameworks. This work introduces the use of novel geometric queries to aid a sample-based motion-planning algorithm guide the growth of a rapidly-exploring random tree through the narrow passages in cluttered and uncertain scenarios. To this effect, specific silhouette and tolerance verification queries are designed for the geometric objects that represent vehicle motion and environmental obstacles. The combination of the silhouette-informed path planner with a CNC-inspired path-smoothing method, and a centralized cooperative speed-assignment algorithm yields a set of C2 continuous trajectories that maintain safe separation with all uncertain obstacles and cooperating peers, meet desired mission constraints, and satisfy a set of simplified dynamic constraints. The vehicles are then tasked to follow their assigned paths and coordinate online to meet mission objectives, desired inter-agent spacing constraints, and temporal constraints—such as a time of arrival or a window of arrival. The thesis introduces two types of inter-agent spacing constraints—tight and loose coordination—and three types of temporal constraints—unenforced, relaxed, and strict—that result in six general time-critical coordination strategies. This thesis presents six distributed coordination protocols to enforce this range of constraints. These coordination protocols rely on a lossy communication network that can be disconnected pointwise in time at all times, but is connected in an integral sense over a sliding temporal window. This work derives transient and steady-state performance bounds for the tight coordination protocols. Simulation results through a cluttered urban-like environment, where vehicles are subject to wind disturbances, corroborate the theoretical results

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Cooperative Carrying Control for Mobile Robots in Indoor Scenario

openIn recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions. In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints. Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage.In recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions. In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints. Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage