Formation tracking of multi-vehicle systems in unknown environments using a multi-region control scheme

ABSTRACT In this paper, a multi-region control scheme is proposed for a formation of nonholonomic vehicles to track a reference trajectory while avoiding collisions and preserving network connectivity in unknown environments. The proposed control scheme defines three regions, safe region, dangerous region and transition region. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees bounded tracking of the reference trajectory for each vehicle. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to characterise constraints of obstacle and inter-vehicle collision avoidance as well as connectivity maintenance. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance control is achieved in transition region. It has been proved that each vehicle can track its reference trajectory while satisfying the constraints simultaneously with a bounded controller which means that the proposed control scheme satisfies input constraints by properly tuning parameters. Simulation results demonstrate the effectiveness of the proposed method.

[1]  Wenwu Yu,et al.  An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination , 2012, IEEE Transactions on Industrial Informatics.

[2]  Dusan M. Stipanovic,et al.  Formation Control and Collision Avoidance for Multi-agent Non-holonomic Systems: Theory and Experiments , 2008, Int. J. Robotics Res..

[3]  Hyo-Sung Ahn,et al.  A survey of multi-agent formation control : Position-, displacement-, and distance-based approaches , 2012 .

[4]  Randal W. Beard,et al.  A decentralized approach to formation maneuvers , 2003, IEEE Trans. Robotics Autom..

[5]  Jie Yang,et al.  Preserving Multirobot Connectivity in Rendezvous Tasks in the Presence of Obstacles With Bounded Control Input , 2013, IEEE Transactions on Control Systems Technology.

[6]  Wei Ren,et al.  Consensus Tracking Under Directed Interaction Topologies: Algorithms and Experiments , 2008, IEEE Transactions on Control Systems Technology.

[7]  Warren E. Dixon,et al.  Network Connectivity Preserving Formation Stabilization and Obstacle Avoidance via a Decentralized Controller , 2012, IEEE Transactions on Automatic Control.

[8]  Danwei Wang,et al.  Real-time distributed optimal trajectory generation for nonholonomic vehicles in formations , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[10]  H. Jin Kim,et al.  Nonlinear Model Predictive Formation Flight , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  Dusan M. Stipanovic,et al.  Trajectory tracking with collision avoidance for nonholonomic vehicles with acceleration constraints and limited sensing , 2014, Int. J. Robotics Res..

[12]  Wei Ren,et al.  Multi-vehicle consensus with a time-varying reference state , 2007, Syst. Control. Lett..

[13]  Farbod Fahimi Non-linear model predictive formation control for groups of autonomous surface vessels , 2007, Int. J. Control.

[14]  Wei Ren,et al.  Distributed coordination architecture for multi-robot formation control , 2008, Robotics Auton. Syst..

[15]  Yongcan Cao,et al.  Distributed coordinated tracking via a variable structure approach - part I: Consensus tracking , 2010, Proceedings of the 2010 American Control Conference.

[16]  Francis L. Merat,et al.  Introduction to robotics: Mechanics and control , 1987, IEEE J. Robotics Autom..

[17]  Yongcan Cao,et al.  Distributed discrete-time coordinated tracking with a time-varying reference state and limited communication , 2009, Autom..

[18]  Jie Yang,et al.  A bounded controller for multirobot navigation while maintaining network connectivity in the presence of obstacles , 2013, Autom..

[19]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[20]  Jie Yang,et al.  Multirobot consensus while preserving connectivity in presence of obstacles with bounded control inputs , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Jie Chen,et al.  Distributed discrete-time coordinated tracking with Markovian switching topologies , 2012, Syst. Control. Lett..

[22]  Mark W. Spong,et al.  Cooperative Avoidance Control for Multiagent Systems , 2007 .

[23]  Petros G. Voulgaris,et al.  Collision-free trajectory tracking while preserving connectivity in unicycle multi-agent systems , 2013, 2013 American Control Conference.

[24]  Ziyang Meng,et al.  Decentralized finite-time sliding mode estimators and their applications in decentralized finite-time formation tracking , 2010, Syst. Control. Lett..

[25]  Hyondong Oh,et al.  Nonlinear Model Predictive Coordinated Standoff Tracking of a Moving Ground Vehicle , 2011 .