Obstacle Avoidance Control of Redundant Robots Using Particle Swarm Optimization


Four variants of Particle Swarm Optimization (PSO) are proposed to solve the obstacle avoidance control problem of redundant robots. The study involved simulating the performance of a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacle. The robot manipulator is required to move from one position to a desired goal position with minimum error while avoiding collision with obstacles in the workspace. The four variants of PSO are namely PSO-W, PSO-C, qPSO-W and qPSO-C where the latter two are hybrid version of the first two algorithms. The hybrid PSO is created by incorporating quadratic approximation operator (QA) alongside with velocity update routine in updating particles' position. The computational result reveals that PSO-W yields better performance in terms of convergence speed and accuracy.

  • Abstract
  • Key Words
  • 1. Introduction
  • 2. Problem Description
  • 3. PSO and Its Variants
  • 4. Fitness Function
  • 5. Simulation Result
  • 5. Discussion and Conclusion
  • References

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