Cooperative control and communication of intelligent swarms: a survey

Individuals exchange information, experience and strategy based on communication. Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control. In this paper, the multi-robot swarm and its cooperative control and communication methods are reviewed, and we summarize these methods from the task, control, and perception levels. Based on the research, the cooperative control and communication methods of intelligent swarms are divided into the following four categories: task assignment based methods (divided into market-based methods and alliance based methods), bio-inspired methods (divided into biochemical information inspired methods, vision based methods and self-organization based methods), distributed sensor fusion and reinforcement learning based methods, and we briefly define each method and introduce its basic ideas. Based on WOS database, we divide the development of each method into several stages according to the time distribution of the literature, and outline the main research content of each stage. Finally, we discuss the communication problems of intelligent swarms and the key issues, challenges and future work of each method.

[1]  Lynne E. Parker,et al.  Adaptive heterogeneous multi-robot teams , 1998, Neurocomputing.

[2]  Dean F. Hougen,et al.  Swarm intelligence for cooperation of bio-nano robots using quorum sensing , 2006 .

[3]  Victor C. M. Leung,et al.  MRL-CC: a novel cooperative communication protocol for QoS provisioning in wireless sensor networks , 2010, Int. J. Sens. Networks.

[4]  Robert J. Wood,et al.  Two foraging algorithms for robot swarms using only local communication , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.

[5]  Ning Xi,et al.  Cooperative teleoperation of a multirobot system with force reflection via Internet , 2004, IEEE/ASME Transactions on Mechatronics.

[6]  Eliseo Ferrante,et al.  Evolution of Self-Organized Task Specialization in Robot Swarms , 2015, PLoS Comput. Biol..

[7]  Kazuhiro Ohkura,et al.  Adaptive role assignment for self-organized flocking of a real robotic swarm , 2016, Artificial Life and Robotics.

[8]  Lihua Xie,et al.  Multi-Agent Cooperative Target Search , 2014, Sensors.

[9]  Anies Hannawati Purnamadjaja,et al.  Pheromone communication in a robot swarm: necrophoric bee behaviour and its replication , 2005, Robotica.

[10]  Tamás Vicsek,et al.  Outdoor flocking and formation flight with autonomous aerial robots , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Lynne E. Parker,et al.  ALLIANCE: an architecture for fault tolerant multirobot cooperation , 1998, IEEE Trans. Robotics Autom..

[12]  Lúcia Valéria Ramos de Arruda,et al.  Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System , 2019, Robotics Auton. Syst..

[13]  Maja J. Mataric,et al.  Sold!: auction methods for multirobot coordination , 2002, IEEE Trans. Robotics Autom..

[14]  Kai Zhang,et al.  An Efficient Stochastic Clustering Auction for Heterogeneous Robotic Collaborative Teams , 2013, J. Intell. Robotic Syst..

[15]  Tuomas Sandholm,et al.  Algorithm for optimal winner determination in combinatorial auctions , 2002, Artif. Intell..

[16]  Maja J. Mataric,et al.  Pusher-watcher: an approach to fault-tolerant tightly-coupled robot coordination , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[17]  Mauro Birattari,et al.  Task partitioning in swarms of robots: an adaptive method for strategy selection , 2011, Swarm Intelligence.

[18]  Sergey Levine,et al.  Trust Region Policy Optimization , 2015, ICML.

[19]  Carlos Sagüés,et al.  Distributed Consensus on Robot Networks for Dynamically Merging Feature-Based Maps , 2012, IEEE Transactions on Robotics.

[20]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[21]  Thomas Schmickl,et al.  Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm , 2008, Auton. Robots.

[22]  Maja J. Mataric,et al.  A general algorithm for robot formations using local sensing and minimal communication , 2002, IEEE Trans. Robotics Autom..

[23]  Shuzhi Sam Ge,et al.  Vision-Based Leader–Follower Formation Control of Multiagents With Visibility Constraints , 2019, IEEE Transactions on Control Systems Technology.

[24]  Karl Kruusamäe,et al.  TeMoto: Intuitive Multi-Range Telerobotic System with Natural Gestural and Verbal Instruction Interface , 2018, Robotics.

[25]  B. Bassler,et al.  Quorum sensing in bacteria. , 2001, Annual review of microbiology.

[26]  Kai-Yew Lum,et al.  Multiagent Information Fusion and Cooperative Control in Target Search , 2013, IEEE Transactions on Control Systems Technology.

[27]  Marco Dorigo,et al.  From Fireflies to Fault-Tolerant Swarms of Robots , 2009, IEEE Transactions on Evolutionary Computation.

[28]  Zhengtao Ding,et al.  Fixed-Time Formation Control of Multirobot Systems: Design and Experiments , 2019, IEEE Transactions on Industrial Electronics.

[29]  Aijun Zhang,et al.  Insect pheromone in elephants , 1996, Nature.

[30]  Jiafeng Guo,et al.  Reinforcement Learning to Rank with Markov Decision Process , 2017, SIGIR.

[31]  Naira Hovakimyan,et al.  Cooperative target tracking in balanced circular formation: Multiple UAVs tracking a ground vehicle , 2013, 2013 American Control Conference.

[32]  Anthony Stentz,et al.  Multi-robot exploration controlled by a market economy , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[33]  Fumitoshi Matsuno,et al.  Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance , 2014, Swarm Intelligence.

[34]  D. Floreano,et al.  Evolutionary Conditions for the Emergence of Communication in Robots , 2007, Current Biology.

[35]  Shimon Whiteson,et al.  Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.

[36]  Koichi Osuka,et al.  Analysis of Sheepdog-Type Robot Navigation for Goal-Lost-Situation , 2018, Robotics.

[37]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[38]  Geoffrey A. Hollinger,et al.  Distributed Data Fusion for Multirobot Search , 2015, IEEE Transactions on Robotics.

[39]  Anies Hannawati Purnamadjaja,et al.  Guiding robots’ behaviors using pheromone communication , 2007, Auton. Robots.

[40]  Tucker R. Balch,et al.  Communication in reactive multiagent robotic systems , 1995, Auton. Robots.

[41]  Meng Joo Er,et al.  Self-Constructing Adaptive Robust Fuzzy Neural Tracking Control of Surface Vehicles With Uncertainties and Unknown Disturbances , 2015, IEEE Transactions on Control Systems Technology.

[42]  Zhong Liu,et al.  A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles , 2018, Sensors.

[43]  Eric C. Kerrigan,et al.  Optimizing Communication and Computation for Multi-UAV Information Gathering Applications , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[44]  Stefano Nolfi,et al.  Self-Organizing Sync in a Robotic Swarm: A Dynamical System View , 2009, IEEE Transactions on Evolutionary Computation.

[45]  W ReynoldsCraig.  Flocks, herds and schools: A distributed behavioral model , 1987 .

[46]  Sven Koenig,et al.  Building Terrain-Covering Ant Robots: A Feasibility Study , 2004, Auton. Robots.

[47]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[48]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[49]  Luca Maria Gambardella,et al.  Self-organized cooperation between robotic swarms , 2011, Swarm Intelligence.

[50]  Meng Joo Er,et al.  Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System , 2016, IEEE Transactions on Cybernetics.

[51]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[52]  Guy Theraulaz,et al.  The Embodiment of Cockroach Aggregation Behavior in a Group of Micro-robots , 2008, Artificial Life.

[53]  Stefan Lee,et al.  Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[54]  Ning Wang,et al.  Adaptive Robust Finite-Time Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles , 2016, IEEE Transactions on Control Systems Technology.

[55]  Luca Maria Gambardella,et al.  Cooperative sensing and recognition by a swarm of mobile robots , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[56]  Neng Wan,et al.  An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles , 2019, Aerospace Science and Technology.

[57]  Yan Wang,et al.  Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes , 2013, Comput. Oper. Res..

[58]  Bernhard Nebel,et al.  Cooperative sensing in dynamic environments , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[59]  David W. Payton,et al.  Pheromone Robotics , 2001, Auton. Robots.

[60]  Manuela M. Veloso,et al.  Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork , 1999, Artif. Intell..

[61]  Stergios I. Roumeliotis,et al.  Distributed multirobot localization , 2002, IEEE Trans. Robotics Autom..

[62]  Cheng-Yi Xia,et al.  Cooperation and strategy coexistence in a tag-based multi-agent system with contingent mobility , 2016, Knowl. Based Syst..

[63]  Bart De Schutter,et al.  Multi-agent Reinforcement Learning: An Overview , 2010 .

[64]  Nathan Michael,et al.  Vision-Based, Distributed Control Laws for Motion Coordination of Nonholonomic Robots , 2009, IEEE Transactions on Robotics.

[65]  Anders Lyhne Christensen,et al.  Overcoming Limited Onboard Sensing in Swarm Robotics Through Local Communication , 2015, Trans. Comput. Collect. Intell..

[66]  Carlos Sagüés,et al.  Distributed consensus algorithms for merging feature-based maps with limited communication , 2011, Robotics Auton. Syst..

[67]  Aníbal Ollero,et al.  Decentralized multi-robot cooperation with auctioned POMDPs , 2013, Int. J. Robotics Res..

[68]  ROBERT M. MAY,et al.  Will a Large Complex System be Stable? , 1972, Nature.

[69]  Gerhard Neumann,et al.  Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning , 2017, ANTS Conference.

[70]  M. Bakhshipour,et al.  Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach , 2017, Appl. Soft Comput..

[71]  L. Riddiford,et al.  Pheromone binding and inactivation by moth antennae , 1981, Nature.

[72]  Anthony Stentz,et al.  Market-based Multirobot Coordination for Complex Tasks , 2006, Int. J. Robotics Res..

[73]  Mohsen Sardari,et al.  Data gathering in networks of bacteria colonies: Collective sensing and relaying using molecular communication , 2012, 2012 Proceedings IEEE INFOCOM Workshops.

[74]  Vijay Kumar,et al.  Dynamic role assignment for cooperative robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[75]  Nidhi Kalra,et al.  Market-Based Multirobot Coordination: A Survey and Analysis , 2006, Proceedings of the IEEE.

[76]  Franco Zambonelli,et al.  Physical deployment of digital pheromones through RFID technology , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[77]  Ali Emre Turgut,et al.  Self-organized flocking in mobile robot swarms , 2008, Swarm Intelligence.

[78]  Christiaan J. J. Paredis,et al.  Heterogeneous Teams of Modular Robots for Mapping and Exploration , 2000, Auton. Robots.

[79]  Melanie E. Moses,et al.  Formica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again , 2012, ANTS.

[80]  Mary-Anne Williams,et al.  Distributed Sensor Fusion for Object Tracking , 2005, RoboCup.

[81]  Dong-Hyun Lee,et al.  Resource-based task allocation for multi-robot systems , 2018, Robotics Auton. Syst..

[82]  Metin Sitti,et al.  Programmable Collective Behavior in Dynamically Self‐Assembled Mobile Microrobotic Swarms , 2018, Advanced science.

[83]  Anders Lyhne Christensen,et al.  Towards Artificial Evolution of Complex Behaviors Observed in Insect Colonies , 2011, EPIA.

[84]  Manuela M. Veloso,et al.  A real-time world model for multi-robot teams with high-latency communication , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).