Decentralised Navigation Control of a Multi-Robot Team to Minimising Energy Consumption in an Unknown Obstacle-Ridden Area

This study presents a decentralised navigation algorithm for a team of mobile robots to traverse an unknown obstacle-ridden environment to detect and trap a target located in the region. The proposed navigational strategy guarantees that the robots maintain the minimum distance allowed to the obstacles while avoiding them to trap the target. The area was occupied by many obstacles with multiple shapes that were randomly distributed in the region; therefore, each robot had to find the safest path between the obstacles based on a decision-making algorithm when there was more than one path to choose from. Unlike the conventional method of collecting information by mobile robots based on sampling in short and pre-set periods, in the proposed method robots collected information at indeterminate intervals leading to reductions in the sensing period, computation and consequent energy consumption. The mathematical proof and the computer simulation confirmed the reliability and robustness of the proposed method.

[1]  Jie Huang,et al.  Formation control of multiple Euler-Lagrange systems via null-space-based behavioral control , 2015, Science China Information Sciences.

[2]  Andrey V. Savkin,et al.  A globally converging algorithm for reactive robot navigation among moving and deforming obstacles , 2015, Autom..

[3]  A. Matveev,et al.  Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey , 2014, Robotica.

[4]  Lu Liu,et al.  Leader-follower formation of vehicles with velocity constraints and local coordinate frames , 2017, Science China Information Sciences.

[5]  Andrey V. Savkin,et al.  Seeking a path through the crowd: Robot navigation in unknown dynamic environments with moving obstacles based on an integrated environment representation , 2014, Robotics Auton. Syst..

[6]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[7]  M. Castillo-Effer,et al.  Wireless sensor networks for flash-flood alerting , 2004, Proceedings of the Fifth IEEE International Caracas Conference on Devices, Circuits and Systems, 2004..

[8]  Lionel Lapierre,et al.  A Step Toward Mobile Robots Autonomy: Energy Estimation Models , 2016, TAROS.

[9]  Vijay Kumar,et al.  Leader-to-formation stability , 2004, IEEE Transactions on Robotics and Automation.

[10]  Farzaneh Abdollahi,et al.  Motion synchronization in unmanned aircrafts formation control with communication delays , 2013, Commun. Nonlinear Sci. Numer. Simul..

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

[12]  Stefano Chiaverini,et al.  The Null-Space based Behavioral control for a team of cooperative mobile robots with actuator saturations , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Hung T. Nguyen,et al.  An Intelligent Robotic Hospital Bed for Safe Transportation of Critical Neurosurgery Patients Along Crowded Hospital Corridors , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Andrey V. Savkin,et al.  A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance , 2011, Autom..

[15]  Chun-Hsu Ko,et al.  Optimized trajectory planning for mobile robot in the presence of moving obstacles , 2015, 2015 IEEE International Conference on Mechatronics (ICM).

[16]  Zhong-Ping Jiang,et al.  Distributed formation control of nonholonomic mobile robots without global position measurements , 2013, Autom..

[17]  Yung-Hsiang Lu,et al.  A case study of mobile robot's energy consumption and conservation techniques , 2005, ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005..

[18]  Ligang Hou,et al.  Time-varying algorithm for swarm robotics , 2018, IEEE/CAA Journal of Automatica Sinica.

[19]  Andrey V. Savkin,et al.  A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles , 2013, Robotica.

[20]  Roberto Sepúlveda,et al.  Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles , 2015, Expert Syst. Appl..

[21]  Mordechai Ben-Ari,et al.  Robots and Their Applications , 2018 .

[22]  Ali Marzoughi A decentralized position estimation switching algorithm to avoid a convex obstacle , 2017, 2017 36th Chinese Control Conference (CCC).

[23]  Gyula Simon,et al.  Sensor network-based countersniper system , 2004, SenSys '04.

[24]  Mandyam V. Srinivasan,et al.  Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS , 2016 .

[25]  Farzaneh Abdollahi,et al.  Mobile robots cooperative control and obstacle avoidance using potential field , 2011, 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

[26]  Shuzhi Sam Ge,et al.  Event-Triggered Coordination for Formation Tracking Control in Constrained Space With Limited Communication , 2019, IEEE Transactions on Cybernetics.

[27]  Cem Ersoy,et al.  Wireless sensor networks for healthcare: A survey , 2010, Comput. Networks.

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

[29]  K. D. Do,et al.  Formation Tracking Control of Unicycle-Type Mobile Robots With Limited Sensing Ranges , 2008, IEEE Transactions on Control Systems Technology.

[30]  Amanda Whitbrook Programming Mobile Robots with Aria and Player: A Guide to C++ Object-Oriented Control , 2009 .

[31]  Xin Chen,et al.  Formation control based on adaptive NN with time-varying interaction among robots , 2008, 2008 27th Chinese Control Conference.

[32]  Andrey V. Savkin,et al.  Safe Robot Navigation Among Moving and Steady Obstacles , 2015 .

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

[34]  Ali Marzoughi Navigating a mobile robot to avoid moving obstacles using virtual source/sink force field , 2017, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[35]  Andrey V. Savkin,et al.  Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles , 2012, Robotics Auton. Syst..

[36]  Domenico Prattichizzo,et al.  Discussion of paper by , 2003 .

[37]  Kar-Han Tan,et al.  High Precision Formation Control of Mobile Robots Using Virtual Structures , 1997, Auton. Robots.

[38]  Changchun Hua,et al.  Adaptive Leader-Following Consensus for Second-Order Time-Varying Nonlinear Multiagent Systems , 2017, IEEE Transactions on Cybernetics.

[39]  Raffaello D'Andrea,et al.  Iterative MILP methods for vehicle-control problems , 2005, IEEE Transactions on Robotics.