改进势场蚁群法的机器人避障及路径规划

Robot Obstacle Avoidance and Path Planning Based on Improved Potential Field Ant Colony Method

针对传统蚁群算法机器人在路径规划过程中出现收敛速度缓慢和陷入局部最优的问题,将蚁群算法与加入虚拟牵引力和快速函数的人工势场法相结合,引入势场合力作为蚂蚁搜索路径点的部分启发信息,使结合后的算法具有较高的全局搜索能力,避免了传统蚁群算法由于启发信息误导所致的局部最优问题,同时提高了收敛速度。为了验证此方法的有效性,用Matlab软件进行仿真实验,结果表明机器人运动轨迹平滑,接近最优路径。

Aiming at the problem that the traditional ant colony algorithm has slow convergence speed and may fall into local optimum in the path planning process,the ant colony algorithm is combined with the artificial potential field method added with virtual traction and fast function,and the resultant force of the potential field is introduced as part of the heuristic information of the ant search path point.The combined algorithm has higher global search ability,avoids the local optimum problem caused by the misleading of the heuristic information in the traditional ant colony algorithm,and improves the convergence speed.In order to verify the effectiveness of this method,a simulation experiment with Matlab software was carried out.The result shows that the robot’s motion trajectory is smooth and is close to the optimal path.