Particle Swarm Optimization in Global Path Planning for Swarm of Robots
Ritesh Kumar Halder
Additional contact information
Ritesh Kumar Halder: IIT Kanpur
Chapter Chapter 12 in Applying Particle Swarm Optimization, 2021, pp 209-232 from Springer
Abstract Planning of the optimal path of an autonomous swarm of mobile robots is quite challenging since they may need to meet multiple targets while avoiding obstacles. This chapter addresses the problem using a method of global navigation based on particle swarm optimization technique. Since it is a meta-heuristic search technique, the path can be found following any optimization criteria such as shortest distance or minimum time. The chapter explores different traditional path planning approaches in contrast with the evolutionary algorithms. The PSO algorithm is tested in different scenarios. Several modifications were implemented in the algorithm for optimization improvements and faster convergence, leading to better results. Geometrical illustrations were used to explain the changes in position of the particles with respect to the environment and obstacles. Consequently, the experiments are conducted on a simulated robot, and the visualizations demonstrated the feasibility of the technique to solve global path planning problem.
Keywords: PSO; Autonomous navigation; Evolutionary algorithm; Obstacle avoidance; Artificial intelligence (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-70281-6_12
Ordering information: This item can be ordered from
Access Statistics for this chapter
More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().