Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach
This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative MultiRobot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex vari...
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| Published in: | IEEE robotics and automation letters Vol. 5; no. 2; pp. 2529 - 2536 |
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| Abstract | This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative MultiRobot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces. |
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| AbstractList | This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative MultiRobot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces. This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi-Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces. |
| Author | Ji, Ze Cai, Boliang Wei, Changyun |
| Author_xml | – sequence: 1 givenname: Changyun orcidid: 0000-0002-5788-6573 surname: Wei fullname: Wei, Changyun email: c.wei@hhu.edu.cn organization: College of Mechanical and Electrical Engineering, Hohai University, Changzhou, China – sequence: 2 givenname: Ze orcidid: 0000-0002-8968-9902 surname: Ji fullname: Ji, Ze email: jiz1@cardiff.ac.uk organization: School of Engineering, Cardiff University, Cardiff, United Kingdom – sequence: 3 givenname: Boliang surname: Cai fullname: Cai, Boliang email: boliang_cai@outlook.com organization: College of Mechanical and Electrical Engineering, Hohai University, Changzhou, China |
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| SubjectTerms | Algorithms cooperating robots Multi-robot systems Multiple objective analysis Multiple robots Optimization optimization and optimal control Particle swarm optimization Robots |
| Title | Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach |
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