Ant Colony Optimization algorithm for robot path planning

Path planning is an essential task for the navigation and motion control of autonomous robot manipulators. This NP-complete problem is difficult to solve, especially in a dynamic environment where the optimal path needs to be rerouted in real-time when a new obstacle appears. The ACO (Ant Colony Opt...

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Veröffentlicht in:2010 International Conference On Computer Design and Applications Jg. 3; S. V3-436 - V3-440
Hauptverfasser: Brand, M, Masuda, M, Wehner, N, Xiao-Hua Yu
Format: Tagungsbericht
Sprache:Englisch
Japanisch
Veröffentlicht: IEEE 01.06.2010
Schlagworte:
Ant colony optimization
Application software
Computer simulation
Manipulator dynamics
Motion control
Navigation
NP-complete problem
Particle swarm optimization
Path planning
Robot Path Planning
Robots
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Zusammenfassung:Path planning is an essential task for the navigation and motion control of autonomous robot manipulators. This NP-complete problem is difficult to solve, especially in a dynamic environment where the optimal path needs to be rerouted in real-time when a new obstacle appears. The ACO (Ant Colony Optimization) algorithm is an optimization technique based on swarm intelligence. This paper investigates the application of ACO to robot path planning in a dynamic environment. Two different pheromone re-initialization schemes are compared and computer simulation results are presented.
DOI:10.1109/ICCDA.2010.5541300