Mobile robot path planning based on adaptive bacterial foraging algorithm
来源期刊：中南大学学报(英文版)2013年第12期
论文作者：LIANG Xiao-dan(梁晓丹) LI Liang-yu(李亮玉) WU Ji-gang(武继刚) CHEN Han-ning(陈瀚宁)
文章页码：3391 - 3400
Key words：robot path planning; bacterial foraging behaviors; swarm intelligence; adaptation
Abstract: The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.
LIANG Xiao-dan(梁晓丹)1, LI Liang-yu(李亮玉)2, WU Ji-gang(武继刚)1, CHEN Han-ning(陈瀚宁)3
(1. School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300160, China;
2. School of Mechanical and Engineering, Tianjin Polytechnic University, Tianjin 300160, China;
3. Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation,
Chinese Academy of Sciences, Shenyang 110016, China)
Abstract:The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.
Key words:robot path planning; bacterial foraging behaviors; swarm intelligence; adaptation
