Global Optimal Path Planning for Multi-agent Flocking: A Multi-Objective Optimization Approach with NSGA-III

Flocking is a multi-objective operation performed by multiple agents in uncertain environments. Objectives of flocking include reaching target for each agent, avoiding collision with obstacles and other agents, as well as maintaining certain pattern among all agents. Multi-objective optimization can...

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Bibliographic Details
Published in2019 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 64 - 71
Main Authors Kesireddy, Adarsh, Shan, Wanliang, Xu, Hao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Collision avoidance
evolutionary algorithm
flocking
Genetic algorithms
Mobile robots
Multi-agent systems
multi-objective optimization
Multi-robot systems
NSGA-II
NSGA-III
Pareto optimization
Path planning
Uncertain systems
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Summary:Flocking is a multi-objective operation performed by multiple agents in uncertain environments. Objectives of flocking include reaching target for each agent, avoiding collision with obstacles and other agents, as well as maintaining certain pattern among all agents. Multi-objective optimization can be performed in priori methods, posteriori methods and scalarizing methods. Pareto front optimization is the best way to optimize multiple objectives simultaneously. To date, flocking has been performed with summation of objective values. In this paper, Pareto front optimization is adopted for the first time for flocking simulation for multi-agents in uncertain environments. For a team of agents, e.g. rovers, in an uncertain environment, Cooperative Co-Evolutionary Algorithm (CCEA) performs well for both exploration and exploitation. CCEAs coupled with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and NSGA-III are performed to achieve flocking for multi-agents. A new reward structure is introduced for CCEA. In order to check the reward structure, flocking is performed in different environment, open and closed. In addition, the performances of NSGA-II and NSGA-III are compared for various cases of flocking with different numbers of objectives. Towards the end, the effectiveness of the developed methods is demonstrated through numerical simulations.
DOI:10.1109/SSCI44817.2019.9002956