欢迎来到OAJRC平台！

期刊栏目

投稿

审稿

期刊目次

加入编委

期刊订阅

添加您的邮件地址以接收即将发行期刊数据：

当前位置：工程学研究 > 2025年 4卷 (2)期

Open Access Article

Journal of Engineering Research. 2025; 4: (2) ; 14-21 ; DOI: 10.12208/j.jer.20250043.

Collaborative search method based on information localized shared swarm robots
基于信息局部共享群机器人的协同搜索方法

作者： 于爱茹 *, 徐望宝

辽宁科技大学电子与信息工程学院辽宁鞍山

*通讯作者：于爱茹,单位：辽宁科技大学电子与信息工程学院辽宁鞍山；

发布时间: 2025-02-23 总浏览量: 26

PDF 全文下载引用本文

摘要

协同搜索是机器人领域的重要研究方向，目前研究多聚焦于算法优化和全局信息共享，局部共享多面向简单环境。针对机器人在复杂未知环境且感知范围受限的协同搜索问题提出了一种基于信息局部共享群机器人的协同搜索方法。首先各机器人根据自身探测或通过局部信息共享获得的有关目标和障碍的信息确定自己的目标，由此计算吸引点，引导机器人避开障碍并靠近目标。其次，机器人跟随领导者运动时设置可视阈值，及时更新吸引点，防止领导者丢失。最后机器人动态调整目标进行任务分配与协调，提高资源利用率和搜索效率。通过仿真验证了该方法在搜索效率、任务分配和障碍规避等方面的可行性，所提方法展现出良好的鲁棒性、灵活性和适应性。

关键词： 协同搜索；信息局部共享；吸引点；任务分配

Abstract

Collaborative search is an important research direction in the field of robotics, and the current research mostly focuses on algorithm optimization and global information sharing, while local sharing is mostly oriented to simple environments. A cooperative search method for swarm robots based on local information sharing is proposed to address the cooperative search problem of robots in complex unknown environments with limited sensing range. Firstly, each robot determines its own target based on the information about the target and obstacles it detects or obtains through local information sharing, from which it calculates the attraction point and guides the robots to avoid obstacles and approach the target. Secondly, the robots set a visual threshold when following the leader's movement to update the attraction point in time to prevent the leader from being lost. Finally, the robot dynamically adjusts the target for task allocation and coordination to improve resource utilization and search efficiency. The feasibility of the method in terms of search efficiency, task allocation and obstacle avoidance is verified through simulation, and the proposed method shows good robustness, flexibility and adaptability.

Key words： Collaborative search; Localized information sharing; Attraction point; Task allocation

参考文献 References

[1] Dorigo M, Theraulaz G, Trianni V. Swarm robotics: Past, present, and future [point of view][J]. Proceedings of the IEEE, 2021, 109(7): 1152-1165.

[2] Zhen Z, Chen Y, Wen L, et al. An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment[J]. Aerospace Science and Technology, 2020, 100: 105826.

[3] Ferrer E C, Hardjono T, Pentland A, et al. Secure and secret cooperation in robot swarms[J]. Science Robotics, 2021, 6(56): eabf1538.

[4] Zhou Y, Chen A, Zhang H, et al. Multitarget search of swarm robots in unknown complex environments[J]. Complexity, 2020, 2020(1): 8643120.

[5] Du Y. A novel approach for swarm robotic target searches based on the DPSO algorithm[J]. IEEE Access, 2020, 8: 226484-226505.

[6] Xu L, Cao X, Du W, et al. Cooperative path planning optimization for multiple UAVs with communication constraints[J]. Knowledge-Based Systems, 2023, 260: 110164.

[7] 夏清松, 唐秋华, 张利平. 多仓储机器人协同路径规划与作业避碰[J]. 信息与控制, 2019, 48(1): 22-28, 34.

[8] Duan H, Huo M, Fan Y. From animal collective behaviors to swarm robotic cooperation[J]. National Science Review, 2023, 10(5): nwad040.

[9] Song Y, Fang X, Liu B, et al. A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network[J]. Applied Soft Computing, 2020, 90: 106156.

[10] Zhou W, Liu Z, Li J, et al. Multi-target tracking for unmanned aerial vehicle swarms using deep reinforcement learning[J]. Neurocomputing, 2021, 466: 285-297.

[11] 成永盛,王强.一种基于遗传算法的协同搜索方法[J].信息技术与信息化,2023,(12):52-55.

[12] Garg V, Shukla A, Tiwari R. AERPSO—An adaptive exploration robotic PSO based cooperative algorithm for multiple target searching[J]. Expert Systems with Applications, 2022, 209: 118245.

[13] Saadaoui H, El Bouanani F, Illi E. Information sharing based on local PSO for UAVs cooperative search of moved targets[J]. Ieee Access, 2021, 9: 134998-135011.

[14] Wei C, Ji Z, Cai B. Particle swarm optimization for cooperative multi-robot task allocation: a multi-objective approach[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 2530-2537.

[15] Tang H, Sun W, Yu H, et al. A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments[J]. Applied Intelligence, 2019, 49: 2603-2622.

[16] Yang J, Xiong R, Xiang X, et al. Exploration enhanced RPSO for collaborative multitarget searching of robotic swarms[J]. Complexity, 2020, 2020(1): 8863526.

[17] Wang X, Fang X. A multi-agent reinforcement learning algorithm with the action preference selection strategy for massive target cooperative search mission planning[J]. Expert Systems with Applications, 2023, 231: 120643.

[18] 吴昌友, 付熙松, 裴均珂. 基于信息共享搜索策略的自适应灰狼算法研究[J]. 电光与控制, 2022, 29(07): 22-28.

[19] 谢永盛, 曾箫潇, 冯文健. 改进布谷鸟搜索算法在多机器人任务分配及路径规划中的应用[J]. 计算机应用与软件, 2021, 38(02): 285-290.

[20] Fei B, Bao W, Zhu X, et al. Autonomous cooperative search model for multi-UAV with limited communication network[J]. IEEE Internet of Things Journal, 2022, 9(19): 19346-19361.

[21] 王茂, 周少武, 张红强, 等. 未知环境下群机器人多目标搜索协同控制[J]. Control Theory & Applications/ Kongzhi Lilun Yu Yinyong, 2022, 39(4).

[22] Xu W, Chen X, Zhao J, et al. Function-segment artificial moment method for sensor-based path planning of single robot in complex environments[J]. Information Sciences, 2014, 280: 64-81.

[23] 徐望宝, 孙明炎. 群机器人自组织围捕多个入侵者的链阵方法[J]. Control Theory & Applications/Kongzhi Lilun Yu Yinyong, 2023, 40(1).

[24] 徐望宝, 荣根熙, 祝超超, 等. 群机器人复杂搬运队形形成的人工社会职位法[J]. 信息与控制, 2016, 45(6): 647-652.

[25] 董百超, 徐望宝, 张笑笑. 群机器人狭窄通道相遇问题的解决方法[J]. 科学技术与工程, 2017, 17(19): 60-64.

[26] 徐望宝. 移动机器人局部路径规划的人工力矩方法[D]. 大连: 大连理工大学, 2014.