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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Vydáno v:	2010 International Conference On Computer Design and Applications Ročník 3; s. V3-436 - V3-440
Hlavní autoři:	Brand, M, Masuda, M, Wehner, N, Xiao-Hua Yu
Médium:	Konferenční příspěvek
Jazyk:	angličtina japonština
Vydáno:	IEEE 01.06.2010
Témata:	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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Popis
Shrnutí:	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