This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

https://journals.sagepub.com/doi/pdf/10.5772/57313

A Survey and Analysis of Multi-Robot Coordination

Heuristic approaches in robot path planning

In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%~77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.

https://www.cister.isep.ipp.pt/docs/lsar__multi_uav_collaboration_for_search_and_rescue_missions/1507/view.pdf

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

/pdf/cooperative-robots-to-observe-moving-targets-review-4a6z3z2cmu.pdf

Cooperative Robots to Observe Moving Targets: Review.

This paper addresses the problem of exploring an unknown environment by a coordinated team of robots. An important question is, which robot should explore which region? In this paper, we present a novel decentralized task allocation approach based on trading rules for multi-robot exploration. In the decentralized system, robots can make their own decisions according to the local information with limited communication. In contrast to previous approaches, our trade-based approach is designed to simulate the relationship between buyers and sellers in a business system, to achieve dynamic task allocation by using a mechanism of unsolicited bid. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate a good performance of the proposed trade-based approach compared to previous approaches.

Multi-robot decentralized exploration using a trade-based approach

This paper presents a new approach for collaborativ e multi-robot planning issues. The main problem tha t arises from multi-robot exploration is waiting situations. We c onsider that such problem involves two or more auto nomous robots in an unknown environment. The mission objective is to explore the entire map, while trying to minimize i ts executing time. Moreover if each robot uses the same topologi cal graph, then it uses the same exploration path t hat makes waiting situations arising. To solve this problem, we propo se a new approach in this paper based on sampling i teratively maps to allow interactive multi-robot exploration. Our appr oach has been implemented in simulation and the experiments demonstrate that the overall completion time of an exp loration task can be significantly reduced by our s ampling-based method.

https://www.ai.univ-paris8.fr/~n/pub/2010/Sampling-Based-Multi-Robot-Exploration-isr2010.pdf

Sampling-Based Multi-Robot Exploration

In this paper we present a novel approach to multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, a sufficient number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and milestones are extracted by post-processing the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches.

/pdf/acs-prm-adaptive-cross-sampling-based-probabilistic-roadmap-ugfhs0i8go.pdf

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Multi-robot task planning (MRTP) is one of the fundamental problems for multi-robot systems. An important question facing this research topic is, which robot should execute which task so as the expected overall system performance can be maximized? Many approaches have been proposed for such a purpose. This paper investigates the existing works in the field. The approaches have been surveyed and some representatives are compared with detailed results. A brief discussion and further research perspectives are also given at the end

https://www.ai.univ-paris8.fr/~n/pub/2012/mrtp.final.pdf

Arab Ali Cherif

Papers

A Survey and Analysis of Multi-Robot Coordination

Multi-robot decentralized exploration using a trade-based approach

Sampling-Based Multi-Robot Exploration

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

On the Problem of Task Planning in Multi-robot Systems