Title :
Articulated Robot Motion Planning Using Ant Colony Optimisation
Author :
Mohamad, Mohd Murtadha ; Taylor, Nicholas K. ; Dunnigan, Matthew W.
Author_Institution :
Dept. of Electr., Electron., & Comput. Eng., Heriot-Watt Univ., Edinburgh
Abstract :
A new approach to robot motion planning is proposed by applying ant colony optimization (ACO) with the probabilistic roadmap planner (PRM). The aim of this approach is to apply ACO to 3-dimensional robot motion planning which is complicated when involving mobile 6-dof or multiple articulated robots. An ant colony robot motion planning (ACRMP) method is proposed that has the benefit of collective behaviour of ants foraging from a nest to a food source. A number of artificial ants are released from the nest (start configuration) and begin to forage (search) towards the food (goal configuration). During the foraging process, a 1-TREE (uni-directional) searching strategy is applied in order to establish any possible connection from the nest to goal. Results from preliminary tests show that the ACRMP is capable of reducing the intermediate configuration between the Initial and goal configuration in an acceptable running time
Keywords :
artificial life; mobile robots; optimisation; path planning; search problems; 3D robot motion planning; ant colony optimisation; articulated robot motion planning; artificial ants; probabilistic roadmap planner; uni-directional searching strategy; Ant colony optimization; Intelligent robots; Mobile robots; Motion planning; Orbital robotics; Particle swarm optimization; Path planning; Road accidents; Robot motion; Space exploration; Ant colony; robot path planning; search technique;
Conference_Titel :
Intelligent Systems, 2006 3rd International IEEE Conference on
Conference_Location :
London
Print_ISBN :
1-4244-01996-8
Electronic_ISBN :
1-4244-01996-8
DOI :
10.1109/IS.2006.348503