Ant colony optimization with potential field heuristic for robot path planning

被引：15
|
作者：
Luo D.-L. [1 ]
Wu S.-X. [1 ]
机构：
[1] School of Information Science and Technology, Xiamen Univ.
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2010年 / 32卷 / 06期
关键词：
Ant colony algorithm; Artificial potential field; Obstacle avoidance; Path planning; Robot;
D O I：
10.3969/j.issn.1001-506X.2010.06.035
中图分类号：
学科分类号：
摘要：
A kind of ant colony optimization with potential field (ACOPF) heuristic, is proposed for path planning of a mobile robot in unknown environment. In the ACOPF, the potential field resultant and the distance between the robot and the goal are utilized to construct the comprehensive heuristic of robot for obstacle avoidance and moving. With this heuristic, an ant colony optimization (ACO) mechanism is used to search a global optimal path from the start point to the end point for a robot in an unknown environment. The proposed ACOPF combines ACO with potential field method (PFM) effectively and makes the optimal path finding more effective than using general ACO. Simulation results show that the proposed ACOPF is very effective and efficient for robot path planning.
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页码：1277 / 1280
页数：3
相关论文
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