摘要
Based on the Cockroach Swarm Optimization （CSO） algorithm, a new Cockroach Colony Optimization （CCO） algorithm is presented and applied to the Robot Path Planning （RPP） problem in this paper. In the CCO algorithm, an improved grid map is used for environment modeling, and 16-geometry and 8-geometry are introduced, respectively, in food division and cockroach search operation. Moreover, the CCO algorithm adopts a non-probabilistic search strategy, which avoids a lot of invalid searches. Furthermore, by introducing a novel rotation scheme in the above CCO algorithm, an Adaptive Cockroach Colony Optimization （ACCO） algorithm is presented for the 2-D Rod-Like Robot Path Planning （RLRPP） problem. The simulation results show that the CCO algorithm can plan an optimal or approximately optimal collision-free path with linear time com- plexities. With the ACCO algorithm, the robot can accomplish intelligent and adaptive rotations to avoid obstacles and pass through narrow passages along the better path.
Based on the Cockroach Swarm Optimization （CSO） algorithm, a new Cockroach Colony Optimization （CCO） algorithm is presented and applied to the Robot Path Planning （RPP） problem in this paper. In the CCO algorithm, an improved grid map is used for environment modeling, and 16-geometry and 8-geometry are introduced, respectively, in food division and cockroach search operation. Moreover, the CCO algorithm adopts a non-probabilistic search strategy, which avoids a lot of invalid searches. Furthermore, by introducing a novel rotation scheme in the above CCO algorithm, an Adaptive Cockroach Colony Optimization （ACCO） algorithm is presented for the 2-D Rod-Like Robot Path Planning （RLRPP） problem. The simulation results show that the CCO algorithm can plan an optimal or approximately optimal collision-free path with linear time com- plexities. With the ACCO algorithm, the robot can accomplish intelligent and adaptive rotations to avoid obstacles and pass through narrow passages along the better path.
基金
This work was supported by the Hong Kong Re- search Grant Council （project CityU123809）, the Na- tional Natural Science Foundation of China （Grant nos. 60571048, 60873264, 60971088 and 71301078）, the Qing Lan Project, the Natural Science Foundation of Education Bureau of Jiangsu Province （project 13KJB120006） and the Innovation Foundation of Huaian College of Information Technology （project hxyc2013001）.
