摘要
目前,巡检机器人采用传统蚁群算法来实现路径规划较普遍,但传统蚁群算法在实现路径规划时在收敛速度、效率以及局部最优等问题表现较差,因此提出基于蚁群-粒子群组合优化算法应用到巡检机器人路径规划中,利用栅格法建立机器人工作环境,改善信息素的更新方式和解的多样性,仿真结果表明相较传统蚁群算法,采用蚁群-粒子群融合算法更能够更好地缩小最优路径的搜索范围,改善最优解的搜索效率,降低迭代次数,实现最优且无碰撞的全局路径。
At present, the inspection robot using the traditional ant colony algorithm to realize path planning are common, but the traditional ant colo- ny algorithm in path planning in the convergence speed and the efficiency of local optimization and poor performance, therefore is proposed based on ant colony particle swarm optimization algorithm is applied to the combination of robot path planning, the establishment of the working environment of the robot using grid method. Diversity to improve the information update in the settlement, the simulation results show that compared with the traditional ant colony algorithm, the ant colony particle swarm fusion algorithm can better reduce the optimal path search range, improve the search efficiency of the optimal solution, reduce the number of iterations, the global optimal and collision free path
基金
国家自然科学基金资助项目(No.61040013)
关键词
路径规划
巡检机器人
蚁群算法
粒子群算法
最优路径
Path Planning
Inspection Robot
Ant Colony Algorithm
Particle Swarm Optimization Algorithm
Optimal Path
