A Survey on Passing-through Control of Multi-Robot Systems in Cluttered Environments

This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.Comment: 18 pages, 19 figure

Enclosing a moving target with an optimally rotated and scaled multiagent pattern

We propose a novel control method to enclose a moving target in a two-dimensional setting with a team of agents forming a prescribed geometric pattern. The approach optimises a measure of the overall agent motion costs, via the minimisation of a suitably defined cost function encapsulating the pattern rotation and scaling. We propose two control laws which use global information and make the agents exponentially converge to the prescribed formation with an optimal scale that remains constant, while the team's centroid tracks the target. One control law results in a multiagent pattern that keeps a constant orientation in the workspace; for the other, the pattern rotates with constant speed. These behaviours, whose optimality and steadiness are very relevant for the task addressed, occur independently from the target's velocity. Moreover, the methodology does not require distance measurements, common coordinate references, or communications. We also present formal guarantees of collision avoidance for the proposed approach. Illustrative simulation examples are provided

Motion Coordination of Multiple Autonomous Vehicles in a Spatiotemporal Flowfield

The long-term goal of this research is to provide theoretically justified control strategies to operate autonomous vehicles in spatiotemporal flowfields. The specific objective of this dissertation is to use estimation and nonlinear control techniques to generate decentralized control algorithms that enable motion coordination for multiple autonomous vehicles while operating in a time-varying flowfield. A cooperating team of vehicles can benefit from sharing data and tasking responsibilities. Many existing control algorithms promote collaboration of autonomous vehicles. However, these algorithms often fail to account for the degradation of control performance caused by flowfields. This dissertation presents decentralized multivehicle coordination algorithms designed for operation in a spatially or temporally varying flowfield. Each vehicle is represented using a Newtonian particle traveling in a plane at constant speed relative to the flow and subject to a steering control. Initially, we assume the flowfield is known and describe algorithms that stabilize a circular formation in a time-varying spatially nonuniform flow of moderate intensity. These algorithms are extended by relaxing the assumption that the flow is known: the vehicles dynamically estimate the flow and use that estimate in the control. We propose a distributed estimation and control algorithm comprising a consensus filter to share information gleaned from noisy position measurements, and an information filter to reconstruct a spatially varying flowfield. The theoretical results are illustrated with numerical simulations of circular formation control and validated in outdoor unmanned aerial vehicle (UAV) flight tests

Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance, a wide range of intelligent escape methodologies has been developed in recent years. This paper presents a comprehensive survey of state-of-the-art research work on intelligent escape of robotic systems. Four main methods of intelligent escape are reviewed, including planning-based methodologies, partitioning-based methodologies, learning-based methodologies, and bio-inspired methodologies. The strengths and limitations of existing methods are summarized. In addition, potential applications of intelligent escape are discussed in various domains, such as search and rescue, evacuation, military security, and healthcare. In an effort to develop new approaches to intelligent escape, this survey identifies current research challenges and provides insights into future research trends in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System