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Path planning for autonomous mobile robots using multi-objective evolutionary particle swarm optimization.

Ittikon Thammachantuek1, Mahasak Ketcham2

  • 1Department of Information Technology, Faculty of Information Technology and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

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|August 19, 2022
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Summary

A novel path planning algorithm enhances autonomous mobile robot navigation by creating the shortest, smoothest, and safest routes. This multi-objective evolutionary particle swarm optimization (MOEPSO) based approach improves efficiency and avoids local optima in dynamic and static environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Path planning is crucial for autonomous mobile robots (AMRs).
  • Existing algorithms often struggle to balance multiple objectives like path length, smoothness, and safety.
  • Multi-objective evolutionary particle swarm optimization (MOEPSO) offers a framework for complex optimization problems.

Purpose of the Study:

  • To propose a new path planning algorithm for AMRs based on MOEPSO.
  • To ensure generated paths are the shortest, smoothest, and safest.
  • To enhance the efficiency and robustness of path planning in both static and dynamic environments.

Main Methods:

  • Developed a novel path planning algorithm integrating MOEPSO principles.
  • Incorporated evolutionary operators (mutation, crossover, selection) to refine particle efficiency.
  • Implemented a weight adjustment method for particle movement and a random method for managing feasible waypoints near obstacles.

Main Results:

  • The algorithm successfully generated shortest, smoothest, and safest paths in simulations.
  • Achieved shortest path lengths of 14.32m (4 obstacles), 14.60m (5 obstacles), 14.47m (6 obstacles) in static environments.
  • In dynamic environments, the shortest path achieved was 12.24m, with reduced processing time.

Conclusions:

  • The proposed MOEPSO-based algorithm effectively plans optimal paths for AMRs.
  • It demonstrates superior performance in balancing multiple path criteria compared to previous methods.
  • The algorithm's evolutionary operators prevent local optima, ensuring better solutions.