基于记忆模拟退火和A*算法的农业机器人遍历路径规划

马全坤; 张彦斐; 宫金良

doi:10.7671/j.issn.1001-411X.201911022

基于记忆模拟退火和A*算法的农业机器人遍历路径规划

Traversal path planning of agricultural robot based on memory simulated annealing and A* algorithm

摘要

摘要:
目的解决农业机器人大田作业时遍历路径规划的问题。

方法提出一种记忆模拟退火与A*算法相结合的遍历算法。首先通过记忆模拟退火算法搜索出任务最优目标点行走顺序，然后使用A*算法进行跨区域衔接路径规划。

结果仿真试验结果表明，该算法规划的遍历路径曼哈顿距离比传统模拟退火算法减少了9.4%，遍历路径覆盖率能达到100%，重复率控制为4.2%。

结论记忆模拟退火通过为传统模拟退火算法增加记忆器，增强了跳出局部最优陷阱的能力，提高了算法所得解的质量。该研究结果可为农业机器人遍历路径规划提供理论基础。

Abstract:
Objective To solve the problem of traversal path planning of agricultural robot in field operation.

Method A memory simulated annealing algorithm combined with A* algorithm was proposed. Firstly, the optimal walking sequence of target points in task was found by memory simulated annealing algorithm, and then A* algorithm was used for crossing regional linking of path planning.

Result The simulation experiments showed that the Manhattan distance of traversal path planned by this algorithm was reduced by 9.4% compared with the traditional simulated annealing algorithm, the coverage of traversal path could reach 100%, and the repetition rate could be controlled at 4.2%.

Conclusion Memory simulated annealing algorithm enhances the ability to jump out of the local optimal trap, and improves the quality of the solution obtained by adding memory device to the traditional simulated annealing algorithm. The research results can provide a theoretical basis for the path planning of agricultural robot.

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