Articulated Robot Motion Planning Using Ant Colony Optimisation

2006 3rd International IEEE Conference Intelligent Systems Pub Date : 2006-09-01 DOI:10.1109/IS.2006.348503

M.M. Mohamad, N. Taylor, M. Dunnigan

引用次数: 18

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

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基于蚁群优化的关节机器人运动规划

将蚁群算法与概率路径规划算法相结合，提出了一种机器人运动规划的新方法。该方法的目的是将蚁群算法应用于复杂的移动六自由度或多关节机器人三维运动规划中。提出了一种蚁群机器人运动规划(ACRMP)方法，该方法具有蚁群从蚁巢到食物源觅食的集体行为。许多人工蚂蚁从巢穴(开始配置)中被释放出来，开始向食物(目标配置)觅食(搜索)。在觅食过程中，采用1-TREE(单向)搜索策略，以建立从巢到目标的任何可能的连接。初步试验结果表明，ACRMP能够在可接受的运行时间内减少初始配置和目标配置之间的中间配置

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2006 3rd International IEEE Conference Intelligent Systems

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