基于鱼群算法的智能机器人全覆盖路径规划

Full coverage path planning of intelligent robot based on fish swarm algorithm

摘要:

为保证机器人的行驶轨迹可以全方位地的覆盖地图的全部坐标点,并降低路径重复率,基于鱼群算法设计智能机器人全覆盖路径规划方法。建立智能机器人死区脱困模型,计算栅格地图模型中的目标活性值,获取整体栅格数量,描述地图中栅格状态,得到脱困时的行驶角度差。基于鱼群算法设计全路径覆盖判定方法,描述不同目标鱼个体之间的距离,在三重移动目标坐标系下,获取元素坐标向量,建立每个目标点的求解代价和,计算下一个目标点行驶的最小距离。设计机器人全覆盖路径规划算法,判断当前位置是否为死区,获取路径规划的全局最优解,实现智能机器人的全覆盖路径规划。利用Matlab仿真软件完成智能机器人全覆盖路径规划实验。结果表明,在简单环境下,该路径规划方法覆盖率为100%,重复率为5.23%,路径长度为15.36m；在复杂环境下,该路径规划方法的覆盖率为100%,重复率则为10.24%,路径长度为20.34m。由此证明,该方法具有较好地规划效果较好。

Abstract:

In order to ensure that the robot"s travel path can cover all the coordinate points of the map in all directions and reduce the path repetition rate, an intelligent robot"s full coverage path planning method is designed based on the fish swarm algorithm. Establish the intelligent robot"s dead zone extrication model, calculate the target activity value in the grid map model, obtain the overall grid number, describe the grid state in the map, and obtain the driving angle difference during extrication. Based on the fish swarm algorithm, a full path coverage determination method is designed to describe the distance between different target fish individuals. Under the triple moving target coordinate system, the element coordinate vector is obtained, the solution cost sum of each target point is established, and the minimum distance traveled by the next target point is calculated. Design robot full coverage path planning algorithm, judge whether the current position is dead zone, obtain the global optimal solution of path planning, and realize the full coverage path planning of intelligent robot. Complete the full coverage path planning experiment of intelligent robot with Matlab simulation software. The results show that in a simple environment, the coverage rate of this path planning method is 100%, the repetition rate is 5.23%, and the path length is 15.36m; in a complex environment, the coverage rate of this path planning method is 100%, the repetition rate is 10.24%, and the path length is 20.34m. This proves that this method has good planning effect.