![]() In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). The results from both simulation and experimentation indicate the high performance of the proposed algorithms and their applicability in search and rescue missionsĪ bio-inspired swarm robot coordination algorithm for multiple target searching The efficacy of the proposed approach is quantitatively evaluated through simulation and real experimentation using heterogeneous Khepera-III mobile robots. A generic market-based approach is proposed in this paper to solve this problem. MRTA is the problem of optimally allocating a set of tasks to a group of robots to optimize the overall system performance while being subjected to a set of constraints. In this study, the problem of multi-robot task allocation (MRTA) is tackled. Many researchers from academia and industry are attracted to investigate how to design and develop robust versatile multi-robot systems by solving a number of challenging and complex problems such as task allocation, group formation, self-organization and much more. Hussein, Ahmed Adel, Mohamed Bakr, Mohamed Shehata, Omar M Khamis, Alaa International Nuclear Information System (INIS) Multi-robot Task Allocation for Search and Rescue Missions The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target. ![]() The approach was further enhanced by the multi-robot-multi-target search in noisy environments. ![]() The approach was developed and analyzed on multiple robot single and multiple target search. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless EnvironmentsĮnergy Technology Data Exchange (ETDEWEB)
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