基于滚动优化和分散捕食者猎物模型的全覆盖路径规划算法

针对多机器人执行全覆盖任务效果差的问题,提出一种基于滚动优化和分散捕食者猎物模型的多机器人全覆盖路径规划算法.首先,利用栅格地图表示作业的环境空间,并基于栅格地图修正捕食者猎物算法中的避开捕食者奖励,添加移动代价奖励和死区回溯机制构建分散捕食者猎物模型;然后,引入滚动优化方法,避免机器人陷入局部最优,预测周期内机器人覆盖栅格的累计奖励值作为适应度函数,并使用鲸鱼优化算法(WOA)求解最优移动序列;最后,在不同环境下进行仿真实验,得到的平均路径长度与生物激励神经网络算法(BINN)和牛耕式A*算法(BA*)相比分别减少了16.69%sim17.33%、10.32%sim20.03%,验证了所提出算法在多机器人全覆盖路径规划中的可行性和有效性.

Complete coverage path planning algorithm based on rolling optimization and decentralized predator-prey model

A multi-robot complete coverage path planning algorithm based on rolling optimization and decentralized predator-prey models is proposed to solve the problem of poor performance of multi-robot performing coverage tasks. The raster map is used to represent the environment space of the job. And based on the raster map, the reward function of avoiding predators in the predator-prey algorithm is modified, the moving cost reward function and dead-zone backtracking mechanism are added to build a decentralized predator-prey model. The rolling optimization method is introduced to avoid the robot falling into local optimum. The cumulative reward value of the robot covered grid during the prediction period is used as the fitness function, and the optimal movement sequence is solved using the whale optimization algorithm (WOA). Finally, simulation experiments are carried out in different environments. Compared with the biologically inspired neural network algorithm (BINN) and the boustrophedon-A* algorithm (BA*), the average path length planned by the proposed method is reduced by 16.69%sim17.33% and 10.32%sim20.03%, respectively, which verifies the feasibility and effectiveness of the proposed method in multi-robot full coverage path planning.