Switching formation strategy with the directed dynamic topology for collision avoidance of a multi‐robot system in uncertain environments

: This paper tackles the distributed leader–follower cooperative control problem for networked heterogeneous unmanned aerial vehicle–unmanned ground vehicle (UAV-UGV) systems in unknown environments requiring formation keeping, obstacle avoidance, inter-robot collision avoidance, and reliable robot communications. To adopt various formations, we design a novel negative imaginary (NI) switching formation protocol with a directed dynamic topology. To prevent an inter-mobile robot collision, a new method to formulate the virtual propulsive force between robots is employed. To avoid unexpected obstacles, a new obstacle avoidance technique that allows the UGVs' formation to change its shape and the UGVs' roles is developed. To determine each UGV robot's order in obstacle avoidance formation, a quadrotor UAV, controlled by a strictly negative imaginary controller involving good wind resistance characteristics, tracks the center of formation shape to guarantee the maintaining visibility for multi-robot systems on the ground. The proposed control system's efficacy is investigated through a rigorously comparative study with other control techniques, namely, the performance of artificial potential field and an NI obstacle avoidance strategy using the switching formation control method without switching topology. Finally, we also conduct a stability analysis of the closed-loop control system using the NI-systems theory.

[1]  Ian R. Petersen,et al.  Formation control of multi-UAVs using negative-imaginary systems theory , 2017, 2017 11th Asian Control Conference (ASCC).

[2]  Ying Zhang,et al.  Consensus and obstacle avoidance for multi-robot systems with fixed and switching topologies , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[3]  Ian R. Petersen,et al.  Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory , 2018, Control Engineering Practice.

[4]  Ching-Chih Tsai,et al.  Intelligent Leader-Following Consensus Formation Control Using Recurrent Neural Networks for Small-Size Unmanned Helicopters , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Minyue Fu,et al.  A consensus-based distributed method of clock synchronization for sensor networks , 2017, Int. J. Distributed Sens. Networks.

[6]  Jin Zhou,et al.  Neural network-based region reaching formation control for multi-robot systems in obstacle environment , 2019, Neurocomputing.

[7]  Zhang Ren,et al.  Time-Varying Formation Tracking for Second-Order Multi-Agent Systems Subjected to Switching Topologies With Application to Quadrotor Formation Flying , 2017, IEEE Transactions on Industrial Electronics.

[8]  Debasish Ghose,et al.  Synchronization of multi-agent systems with heterogeneous controllers , 2015, ArXiv.

[9]  Gigliola Vaglini,et al.  Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search , 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

[10]  Domitilla Del Vecchio,et al.  Automated Vehicle-to-Vehicle Collision Avoidance at Intersections , 2011 .

[11]  Karnika Biswas,et al.  On reduction of oscillations in target tracking by artificial potential field method , 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS).

[12]  Shuzhi Sam Ge,et al.  Neural-Network-Based Switching Formation Tracking Control of Multiagents With Uncertainties in Constrained Space , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Alexander Lanzon,et al.  Feedback Control of Negative-Imaginary Systems , 2010, IEEE Control Systems.

[14]  Farzaneh Abdollahi,et al.  Time-varying formation control of a collaborative heterogeneous multi agent system , 2014, Robotics Auton. Syst..

[15]  Yingmin Jia,et al.  Adaptive leader-follower formation control of non-holonomic mobile robots using active vision , 2015 .

[16]  Randal W. Beard,et al.  A coordination architecture for spacecraft formation control , 2001, IEEE Trans. Control. Syst. Technol..

[17]  Zhang Ren,et al.  Finite-Time Time-Varying Formation Tracking for High-Order Multiagent Systems With Mismatched Disturbances , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Gianluca Antonelli,et al.  The null-space-based behavioral control for autonomous robotic systems , 2008, Intell. Serv. Robotics.

[19]  Ahmed Rahmani,et al.  Consensus tracking for second-order nonlinear multi-agent systems with switching topologies and a time-varying reference state , 2016, Int. J. Control.

[20]  Dongkyoung Chwa,et al.  Decentralized behavior-based formation control of multiple robots considering obstacle avoidance , 2018, Intell. Serv. Robotics.

[21]  Daizhan Cheng,et al.  Leader-following consensus of multi-agent systems under fixed and switching topologies , 2010, Syst. Control. Lett..

[22]  Zhang Ren,et al.  Time-varying group formation analysis and design for second-order multi-agent systems with directed topologies , 2016, Neurocomputing.

[23]  Du Ho Some results on closed-loop identification of quadcopters , 2018 .

[24]  Zhang Ren,et al.  Fully Distributed Output-feedback Time-varying Formation Tracking Control for Second-order Nonlinear Multi-agent Systems with Switching Directed Topologies , 2018, 2018 37th Chinese Control Conference (CCC).

[25]  Ian R. Petersen,et al.  Robust cooperative control of multiple heterogeneous Negative-Imaginary systems , 2015, Autom..

[26]  Jun Li,et al.  Dynamic analysis and PID control for a quadrotor , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[27]  Ahmed Rahmani,et al.  Distributed fixed-time formation tracking of multi-robot systems with nonholonomic constraints , 2018, Neurocomputing.

[28]  Yanyan Dai,et al.  A switching formation strategy for obstacle avoidance of a multi-robot system based on robot priority model. , 2015, ISA transactions.

[29]  Kyongsu Yi,et al.  Tire-Road Friction-Coefficient Estimation , 2010, IEEE Control Systems.

[30]  Farzaneh Abdollahi,et al.  A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots , 2014, IEEE Transactions on Industrial Electronics.

[31]  Yanyan Dai,et al.  Formation control of mobile robots with obstacle avoidance based on GOACM using onboard sensors , 2014 .

[32]  Fei Chen,et al.  A Connection Between Dynamic Region-Following Formation Control and Distributed Average Tracking , 2018, IEEE Transactions on Cybernetics.

[33]  Beeshanga Abewardana Jayawickrama,et al.  Algorithm for energy efficient inter-UAV collision avoidance , 2017, 2017 17th International Symposium on Communications and Information Technologies (ISCIT).

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

[35]  Tarek A. Tutunji,et al.  Identification of quadcopter hovering using experimental data , 2015, 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).

[36]  Yang Wang,et al.  Design of leader's path following system for multi-vehicle autonomous convoy , 2017, 2017 IEEE International Conference on Unmanned Systems (ICUS).

[37]  Hyun Myung,et al.  Receding horizon particle swarm optimisation-based formation control with collision avoidance for non-holonomic mobile robots , 2015 .

[38]  Y. Jia,et al.  Brief paper - Distributed containment control of second-order multi-agent systems with inherent non-linear dynamics , 2014 .

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