Swarm Robotic Behaviour Learning in Search and Pre-Surround Stage for Targets Trapping Task
XUE Songdong1, ZHANG Yunzheng1, ZENG Jianchao2
1.Institute of Industry and System Engineering, Taiyuan University of Science and Technology, Taiyuan 030024 2.School of Data Science and Technology, North University of China, Taiyuan 030051
Abstract A strategy for navigation-type collective behaviour learning is developed for swarm robotic coordination in a target search task. Sub-swarms are formed by utilizing the method of dynamic self-organizing task allocation with closed-loop adjusting function, and then a social learning particle swarm optimization based robotic learning strategy is introduced into sub-swarms. In the sub-swarm, all robots are sorted in descending order by the cognition ability of each robot to its common desired target. The robots with better perception of the target are regarded as the behaviour demonstrators. Then, one of the behaviour demonstrators is selected randomly by each robot to learn in every dimension of the working space. Thus, the learning behaviour vector of each robot can be constructed for decision making on its future moving behaviour. The results show that the robot can coordinate with each other and the search efficiency is improved without the global social experience learning.
Fund:Supported by National Natural Science Foundation of China(No.61472269), Research Project of Shanxi Scholarship Council of China(No.2016-091)
Corresponding Authors:
XUE Songdong(Corresponding author), Ph.D., associate professor. His research interests include swarm robotic coordinated control.
About author:: ZHANG Yunzheng, Ph.D. candidate. His research interests include intelligent computing and swarm robotic coordinated control;ZENG Jianchao, Ph.D., professor. His research interests include intelligent computing and swarm robotic coordinated control.
[1] PHAN T A, RUSSELL R A. A Swarm Robot Methodology for Co-llaborative Manipulation of Non-identical Objects. International Journal of Robotics Research, 2012, 31(1): 101-122. [2] BRAMBILLA M, FERRANTE E, BIRATTARI M, et al. Swarm Robotics: A Review from the Swarm Engineering Perspective. Swarm Intelligence, 2013, 7(1): 1-41. [3] 张 勇,巩敦卫,胡 滢,等.室内噪声环境下气味源的多机器人微粒群搜索方法.电子学报, 2014, 42(1): 70-76. (ZHANG Y, GONG D W, HU Y, et al. A PSO-Based Multi-robot Search Method for Odor Source in Indoor Environment with Noise. Acta Electronica Sinica, 2014, 42(1): 70-76.) [4] 张红强,章 兢,周少武,等.基于简化虚拟受力模型的未知复杂环境下群机器人围捕.电子学报, 2015, 43(4): 665-674. (ZHANG H Q, ZHANG J, ZHOU S W, et al. Hunting in Unknown Complex Environments by Swarm Robots Based on Simplified Vir-tual-Force Model. Acta Electronica Sinica, 2015, 43(4): 665-674.) [5] HUANG T Y, CHEN X B, XU W B, et al. A Self-organizing Coo-perative Hunting by Swarming Robotic Systems Based on Loose-Pre-ference Rule. Acta Automatica Sinica, 2013, 39(1): 57-68. [6] 张红强,章 兢,周少武,等.未知动态环境下非完整移动群机器人围捕.控制理论与应用, 2014, 31(9): 1151-1165. (ZHANG H Q, ZHANG J, ZHOU S W, et al. Nonholonomic Mobile Swarm Robots Hunting in Unknown Dynamic Environments.Control Theory & Applications, 2014, 31(9): 1151-1165.) [7] 张云正,薛颂东,曾建潮.群机器人多目标搜索中带闭环调节的动态任务分工.机器人, 2014, 36(1): 57-68. (ZHANG Y Z, XUE S D, ZENG J C. Dynamic Task Allocation with Closed-Loop Adjusting in Swarm Robotic Search for Multiple Targets. Robot, 2014, 36(1): 57-68.) [8] PUGH J, MARTINOLI A. Inspiring and Modeling Multi-robot Search with Particle Swarm Optimization // Proc of the IEEE Swarm Intelligence Symposium. Washington, USA: IEEE, 2007: 332-339. [9] YANG B, DING Y S, JIN Y C, et al. Self-organized Swarm Robot for Target Search and Trapping Inspired by Bacterial Chemotaxis. Robotics and Autonomous Systems, 2015, 72: 83-92. [10] DORIGO M, BIRATTARI M, BRAMBILLA M. Swarm Robotics. Scholarpedia, 2014, 9(1): 1463. [11] PUGH J, MARTINOLI A. Multi-robot Learning with Particle Swarm Optimization // Proc of the International Joint Conference on Autonomous Agents and Multiagent Systems. New York, USA: ACM, 2006: 441-448. [12] HEREFORD J, SIEBOLD M. Multi-robot Search Using a Physica-lly-Embedded Particle Swarm Optimization. International Journal of Computational Intelligence Research, 2008, 4(2): 197-209. [13] MARQUES L, NUNES U, DE ALMEIDA A T. Particle Swarm-Based Olfactory Guided Search. Autonomous Robots, 2006, 20(3): 277-287. [14] JATMIKO W, SEKIYAMA K, FUKUDA T. A PSO-Based Mobile Robot for Odor Source Localization in Dynamic Advection-Diffusion with Obstacles Environment: Theory, Simulation and Measurement. IEEE Computational Intelligence Magazine, 2007, 2(2): 37-51. [15] GARCIA M A P, MONTIEL O, CASTILLO O, et al. Path Pla-nning for Autonomous Mobile Robot Navigation with Ant Colony Optimization and Fuzzy Cost Function Evaluation. Applied Soft Computing, 2009, 9(3): 1102-1110. [16] ZHU Q B, HU J, CAI W B, et al. A New Robot Navigation Algorithm for Dynamic Unknown Environments Based on Dynamic Path Re-computation and an Improved Scout Ant Algorithm. Applied Soft Computing, 2011, 11(8): 4667-4676. [17] SHI Z G, ZHANG X M, TU J, et al. An Improved Capturing Algorithm Based on Particle Swarm Optimization for Swarm Robots System // Proc of the IEEE International Conference on Computer Science and Automation Engineering. Washington, USA: IEEE, 2012: 566-571. [18] COUCEIRO M S, FIGUEIREDO C M, RUI R P, et al. Darwinian Swarm Exploration under Communication Constraints: Initial Deployment and Fault-Tolerance Assessment. Robotics and Autonomous Systems, 2014, 62(4): 528-544. [19] 张云正,薛颂东,曾建潮.群机器人多目标搜索中的合作协同和竞争协同.机器人, 2015, 37(2): 142-151. (ZHANG Y Z, XUE S D, ZENG J C. Cooperative and Competitive Coordination in Swarm Robotic Search for Multiple Targets. Robot, 2015, 37(2): 142-151.) [20] STIRLING T, FLOREANO D. Energy-Time Efficiency in Aerial Swarm Deployment // MARTINOLI A, ed. Distributed Autonomous Robotic Systems. Berlin, Germany: Springer, 2010: 5-18. [21] CHENG R, JIN Y. A Social Learning Particle Swarm Optimization Algorithm for Scalable Optimization. Information Sciences, 2015, 291: 43-60. [22] XUE S D, ZENG J C. Control over Swarm Robots Search with Swarm Intelligence Principles. Journal of System Simulation, 2008, 20(13): 3449-3454. [23] 周少武,张 鑫,张红强,等.基于简化虚拟受力模型的群机器人多目标搜索协调控制.机器人, 2016, 38(6): 641-650. (ZHOU S W, ZHANG X, ZHANG H Q, et al. Coordinated Control of Swarm Robots for Multi-target Search Based on a Simplified Virtual-Force Model. Robot, 2016, 38(6): 641-650.) [24] 薛颂东.面向目标搜索的群机器人协调控制及其仿真研究.博士学位论文.兰州:兰州理工大学, 2009. (XUE S D. Swarm Robotic Search for Target: Cooperative Control Techniques, Strategies, and Simulations. Ph.D. Dissertation. Lanzhou, China: Lanzhou University of Technology, 2009.) [25] 郭晋炜,曾建潮.基于目标搜索任务的群机器人路径规划.系统仿真学报, 2010, 22(12): 2842-2846. (GUO J W, ZENG J C. Swarm Robot Path Planning Based on Target-Searching Task. Journal of System Simulation, 2010, 22(12): 2842-2846.) [26] COUCEIRO M S, VARGAS P A, ROCHA R P, et al. Benchmark of Swarm Robotics Distributed Techniques in a Search Task. Robotics and Autonomous Systems, 2014, 62(2): 200-213.
2018, Vol. 31 
