动态环境下机器人多目标路径规划蚂蚁算法

研究了一种新颖的动态复杂不确定环境下的机器人多目标路径规划蚂蚁算法。该方法首先根据蚂蚁觅食行为对多个目标点的组合进行优化,规划出一条最优的全局导航路径。在此基础上,机器人按照规划好的目标点访问顺序根据多蚂蚁协作局部路径算法完成局部路径的搜索。机器人每前进一步都实时地进行动态障碍物运动轨迹预测以及碰撞预测,并重新进行避碰局部路径规划。仿真结果表明,即使在障碍物非常复杂的地理环境,用该算法也能使机器人沿一条全局优化的路径安全避碰的遍历各个目标点,效果十分令人满意。

Ant Algorithm for Mobile Robot Path Plan with Multi-objects in an Unfamiliar Environment

Based on Ant Colony Optimization(ACO),this thesis presents a novel algorithm underlying the robot multi-objects path planning and dynamic obstacle avoidance in a complex and unfamiliar environment,and the algorithm optimizes the combination of all objects and can get a globally optimal navigation path.The locally optimal path between the position of robot and the present object which is gotten from the globally optimal navigation path is accomplished by local path planning with multi-ants in cooperation.Collision prediction proceeds timely after ever step of robot and local dynamic planning for obstacle avoidance is executed.Our computer experiments demonstrate that the algorithms are robust and stable.