For the complete-coverage path planning of mobile robot, an algorithm based on grid belief function was pro-posed. The goal is to control a mobile robot to traverse all reachable locations in work area, while guarantee automatic obstacle avoidance. Firstly, the grid map is assigned with values according to the information of the environment, the obstacles, covered grids and uncovered grids are represented by using different function values; secondly, judging if a mobile robot is caught in dead zone or not, different direction belief functions are introduced to adjust the grid function values; lastly, the robot programs the covered path according to the grid belief function value. The proposed algorithm can guide a robot to realize full coverage of work area, rapidly escape from dead zone and achieve a low repetition rate of the covered path. In the simulation experiments, by compared with bio-inspired neural network algorithm, the al-gorithm proposed in the paper was verified to own high coverage efficiency.%针对移动机器人全覆盖路径规划问题,给出一种基于栅格信度函数的全覆盖路径规划算法.目的是为了控制移动机器人能够遍历工作区域中所有的可到达点,同时保证能够自动避开障碍物.首先,根据环境的信息对栅格地图进行赋值,使用不同的函数值表示障碍物、已覆盖栅格和未覆盖栅格;其次,判断机器人是否陷入死区引入不同方向信度函数,对栅格函数值进行调整;最后,机器人根据栅格信度函数值规划覆盖路径.本文所提及的算法不仅能够引导移动机器人实现工作区域的全覆盖而且能够快速逃离死区,实现覆盖路径的低重复率.仿真实验中,通过与生物启发神经网络算法的比较,证明本文提及算法有更高的覆盖效率.
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