Path planning of agricultural mobile robot based on hybrid improved sparrow search algorithm

doi:10.13733/j.jcam.issn.2095-5553.2024.09.036

Abstract

Abstract:

Aiming at the shortcomings of current agricultural mobile robot path planning methods that are prone to produce a local optimal path，a mobile robot path planning algorithm based on hybrid multi-strategy improved sparrow search algorithm is proposed. Firstly，for improving the optimizing ability of sparrow search algorithm，the even symmetric infinite folding chaotic sequence，spiral search finder update，multiple learning follower update are introduced to improve the population diversity，search blindness and global search ability of the traditional algorithm，so as to realize the Multi-Strategy Hybrid Improved Sparrow Search Algorithm（MHISSA）. Then，the path planning model of mobile robot is constructed，and the coding method of population individuals is defined by using the navigation point model，and the fitness function of synchronous obstacle avoidance and shortest path is constructed. Combined with cubic spline interpolation，MHISSA algorithm is applied to solve the global path planning problem of mobile robot. Simple and complex obstacle environments are constructed，and the experimental comparative analysis of single robot path planning and multi-robot cooperative path planning are carried out. The results show that the improved algorithm can get a smooth and collision-free optimal path，the optimal value and the average value of path planning length are 23. 08% and 19. 56% lower than those of the traditional sparrow search algorithm respectively. The field scene case verification proves that the method has good practicability in the field of path planning for agricultural mobile robots.

Key words: agricultural robot, path planning, sparrow search algorithm, spiral search, multiple learning

摘要：

针对目前农用移动机器人进行全局路径规划容易产生局部最优路径的不足，提出混合多策略改进麻雀搜索算法的移动机器人路径规划算法。首先，为提升麻雀搜索算法的寻优能力，引入偶对称无限折叠混沌序列、螺旋式搜索发现者更新、多重学习追随者更新机制对传统算法的种群多样性、搜索盲目性及全局搜索能力改进，实现多策略混合改进麻雀搜索算法 MHISSA。然后，构建移动机器人的路径规划模型，利用导航点定义种群个体的编码方式，并构造同步避障与路径最短的适应度函数，结合三次样条插值法将 MHISSA应用于求解农用移动机器人全局路径规划问题。最后，构造简单和复杂障碍物环境对单机器人路径规划和多机器人协同路径规划进行试验对比分析。结果表明，改进算法能够得到平滑无碰撞的最优路径，路径规划长度最优值和均值较传统麻雀搜索算法平均分别降低 23. 08%和 19. 56%。实地场景案例也证明方法在农用移动机器人路径规划方面具备较好的实用性。

关键词: 农用机器人, 路径规划, 麻雀搜索算法, 螺旋搜索, 多重学习

CLC Number:

S22
TP273

Mou Yuanming, Zhuo Ran, Gao Fei. Path planning of agricultural mobile robot based on hybrid improved sparrow search algorithm [J]. Journal of Chinese Agricultural Mechanization, 2024, 45(9): 234-243.

牟远明 , 卓然 , 高飞 . 基于混合改进麻雀搜索算法的农用移动机器人路径规划[J]. 中国农机化学报, 2024, 45(9): 234-243.

References

［ 1］于振中，李强，樊启高 .智能仿生算法在移动机器人路径规划优化中的应用综述［ J］.计算机应用研究， 2019，36（11）：3210-3219.

Yu Zhenzhong， Li Qiang， Fan Qigao. Survey on application of bioinspired intelligent algorithms in path planning optimization of mobile robots［J］. Application Research of Computers，2019，36（11）：3210-3219.

［ 2］李培英 .基于改进遗传算法的移动机器人路径规划［ J］.国外电子测量技术， 2022，41（6）：38-44.

Li Peiying. Path planning of mobile robot based on improved genetic algorithm［J］. Foreign Electronic Measurement Technology，2022，41（6）：38-44.

［ 3］杨凌耀，张爱华，张洁，等 .栅格地图环境下机器人速度势实时路径规划［ J］.计算机工程与应用， 2021，57（24）： 290-295. Yang Lingyao，Zhang Aihua，Zhang Jie，et al. Real.time path planning of velocity potential for robot in grid map environment［J］. Computer Engineering and Applications， 2021，57（24）：290-295.

［ 4］刘翰培，王东署，汪宇轩，等 .移动机器人路径规划的模糊人工势场法研究［ J］.控制工程， 2022，29（1）：33-38.

Liu Hanpei， Wang Dongshu， Wang Yuxuan， et al. Research of path planning for mobile robots based on fuzzy artificial potential field method［J］. Control Engineering of China，2022，29（1）：33-38.

［ 5］杨保海，任全会，李海生 .复杂环境下果园机器人路径规划方法研究［ J］.中国农机化学报，2021，42（2）： 134-138.

Yang Baohai， Ren Quanhui， Li Haisheng. Research on path planning method of orchard robot in complex environment［J］. Journal of Chinese Agricultural Mechanization，2021，42（2）：134-138.

［ 6］王硕，周海波，张建军，等 .拓展 A*算法的机器人室内三维地图路径规划［ J］.计算机仿真，2022，39（2）： 394-398.

Wang Shuo，Zhou Haibo，Zhang Jianjun，et al. Extended A* algorithm for indoor 3D map path planning of robots［J］. Computer Simulation，2022，39（2）：394-398.

［ 7］陈超，唐坚，靳祖光，等 .一种基于可视图法导盲机器人路径规划的研究［ J］.机械科学与技术， 2014，33（4）： 490-495.

Chen Chao， Tang Jian， Jin Zuguang， et al. A path planning algorithm for seeing eye robots based on V-Graph［J］. Mechanical Science and Technology for Aerospace Engineering，2014，33（4）：490-495.

［ 8］成怡，郝密密 .改进深度强化学习的室内移动机器人路径规划［ J］.计算机工程与应用， 2021，57（21）：256-262.

Cheng Yi，Hao Mimi. Path planning for indoor mobile robot with improved deep reinforcement learning［J］. Computer Engineering and Applications，2021，57（21）：256-262.

［ 9］李晓静，余东满 .基于自适应蚁群算法的农用智能机器人路径规划［ J］.中国农机化学报， 2019，40（9）：189-193.

Li Xiaojing，Yu Dongman. Path planning of agricultural intelligent robot based on algorithm of self.adaptive ant colony［J］. Journal of Chinese Agricultural Mechanization， 2019，40（9）：189-193.

［10］徐兴，俞旭阳，赵芸，等 .基于改进遗传算法的移动机器人全局路径规划［ J］.计算机集成制造系统， 2022，28（6）： 1659-1672.

Xu Xing，Yu Xuyang，Zhao Yun，et al. Global path planning of mobile robot based on improved genetic algorithm［J］. Computer Integrated Manufacturing Systems，2022，28（6）： 1659-1672.

［11］胡章芳，冯淳一，罗元 .改进粒子群优化算法的移动机器人路径规划［J］.计算机应用研究， 2021，38（10）：3089-3092.

Hu Zhangfang，Feng Chunyi，Luo Yuan. Improved particle swarm optimization algorithm for mobile robot path planning［J］. Application Research of Computers，2021，38（10）：3089-3092.

［12］罗如学，陈妙娜，林继灿 .基于改进人工鱼群算法的机器人路径规划［ J］.科学技术与工程， 2020，20（23）：9445-9449.

Luo Ruxue，Chen Miaona，Lin Jican. Robot path planning based on improved artificial fish swarm algorithm［J］. Science Technology and Engineering， 2020， 20（23）： 9445-9449.

［13］刘景森，袁蒙蒙，李煜 .基于改进的樽海鞘群算法求解机器人路径规划问题［ J］.计算机研究与发展， 2022，59（6）： 1297-1314.

Liu Jingsen，Yuan Mengmeng，Li Yu. Robot path planning based on improved salp swarm algorithm［J］. Journal of Computer Research and Development， 2022， 59（6）： 1297-1314.

［14］Xue J，Shen B. A novel swarm intelligence optimization approach：Sparrow search algorithm［J］. Systems science & control engineering，2020，8（1）：22-34.

［15］Li H M，Zhang Y. Study of transformer fault diagnosis based on sparrow optimization algorithm［C］. Proceedings of the 2020 1st International Conference on Control， Robotics and Intelligent System，2020：63-66.

［16］吕鑫，慕晓冬，张钧 .基于改进麻雀搜索算法的多阈值图像分割［ J］.系统工程与电子技术，2021，43（2）： 318-327.

Lü Xin，Mu Xiaodong，Zhang Jun. Multi.threshold image segmentation based on improved sparrow search algorithm［J］. Systems Engineering and Electronics，2021，43（2）：318-327.

［17］尹德鑫，张达敏，张琳娜，等 .认知工业物联网中基于麻雀搜索算法的频谱分配策略［ J］.数据采集与处理， 2022， 37（2）：371-382.

Yin Dexin，Zhang Damin，Zhang Linna，et al. Spectrum allocation strategy based on sparrow algorithm in cognitive industrial Internet of Things［J］. Journal of Data Acquisition and Processing，2022，37（2）：371-382.

［18］邓百川，徐睿，黄成勇，等 .基于柯西变异麻雀搜索算法的无人机三维航迹规划［ J］.机械设计与研究， 2022，38（1）：62-66.

Deng Baichuan，Xu Rui，Huang Chengyong，et al. Three dimensional flight path planning for unmanned aerial vehicles based on Cauchy mutation sparrow search algorithm［J］. Machine Design and Research，2022，38（1）：62-66.

［19］Liu T，Yuan Z，Wu L，et al. An optimal brain tumor detection by convolutional neural network and enhanced sparrow search algorithm［J］. Proceedings of the Institution of Mechanical Engineers，Part H：Journal of Engineering in Medicine，2021，235（4）：459-469.

［20］吕鑫，慕晓冬，张钧，等 .混沌麻雀搜索优化算法［ J］.北京航空航天大学学报， 2021，47（8）：1712-1720.

Lü Xin，Mu Xiaodong，Zhang Jun，et al. Chaos sparrow search optimization algorithm［J］. Journal of Beijing University of Aeronautics and Astronautics，2021，47（8）：1712-1720.

［21］毛清华，张强 .融合柯西变异和反向学习的改进麻雀算法［J］.计算机科学与探索， 2021，15（6）：1155-1164.

［22］Yuan J， Zhao Z， Liu Y， et al. DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm［J］. IEEE Access，2021，9：16623-16629.

［23］Mirjalili S，Lewis A. The whale optimization algorithm［J］. Advances in Engineering Software，2016，95：51-67.

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