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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE robotics and automation letters Ročník 5; číslo 2; s. 2529 - 2536
Hlavní autoři: Wei, Changyun, Ji, Ze, Cai, Boliang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
cooperating robots
Multi-robot systems
Multiple objective analysis
Multiple robots
Optimization
optimization and optimal control
Particle swarm optimization
Robots
ISSN:2377-3766, 2377-3766
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2972894