Research Area 2: Multi-Robot Cooperative Control, Sensing and Learning

sponsors-logo

Introduction

Multi-robot systems have many potential applications in various fields such as target tracking, environmental monitoring, scalar field mapping, and intelligent transportation systems, etc. This project focuses on the development of collaborative control, learning and sensing algorithms to create smart multi-robot systems for real world applications.

Goals

Development of adaptive formation control schemes, and collaborative learning and sensing algorithms for multi-robot systems

Building a framework of hybrid systems consisting of UAVs and ground robots for environment mapping, sensing and monitoring

Development of reliable security algorithms for drones

pr21

 Rovio Robots

 Multi-robot cooperative learning for optimal field coverage

 

Reinforcement learning for autonomous drone navigation

 

Dynamic Target Tracking with drone

 

Publications

  • M. Rahimi, S. Gibb, Y. Shen, and H. M. La. A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing. The 2018 Springer International Conference on Engineering Research and Applications (ICERA), December 1-2, 2018, Thai Nguyen, Vietnam. (Accepted)
  • H. X.  Pham, H. M. La, D. Feil-Seifer, and L. Nguyen. Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation. Proceedings of the 16th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), August 6-8, 2018, Philadelphia, PA, USA.
  • H. X. Pham, H. M. La, D. Feil-Seifer, and L. V. Nguyen. Performance Comparison of Function Approximation-based Q Learning Algorithms for Autonomous UAV Navigation. The 15th International Conference on Ubiquitous Robots (UR), June 26-30, 2018, Hawaii, USA.
  • D. Connell, and H. M. La. RRT*-Based Dynamic Path Planning and Replanning for Mobile Robots. International Journal of Advanced Robotic Systems, SAGE publisher, Volume 15, N0. 3, Pages: 1729881418773874, May 2018.
  • A. Singandhupe, H. M. La, and D. Feil-Seifer. Reliable Security Algorithm for Drones Using Individual Characteristics From an EEG Signal. IEEE Access, Volume: 6, Issue: 1, December, 2018.
  • H. X. Pham, H. M. La, D. Feil-Seifer, and M. Deans. A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 24-28, 2017, Vancouver, Canada.
  • H. X. Pham, H. M. La, D. Feil-Seifer, and M. Deans. A Distributed Control Framework for Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking. IEEE Transactions on Systems, Man and Cybernetics: Systems, April 2018.
  • D. Connell, and H. M. La. Dynamic Path Planning and Replanning for Mobile Robots using RRT*. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 5-8, 2017, Banff, Canada. PDF
  • M. Nguyen, H. M. La, and K. Teague. Collaborative and Compressed Mobile Sensing for Data Collection in Distributed Robotic NetworksIEEE Transactions on Control of Network Systems, pp.1-12, September 2017.
  • T. Nguyen, T. T. Han and H. M. La. Distributed Flocking Control of Mobile Robots by Bounded Feedback. In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, pages 1-6, Sept. 27-30, 2016, Urbana-Champaign, Illinois, USA.
  • A .D Dang, H. M. La and J. Horn. Distributed Formation Control for Autonomous Robots Following Desired Shapes in Noisy Environment. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages 1-6, September 19-21, 2016, Baden-Baden, Germany.
  • A. Woods, H. M. La, Q. P. Ha. A Novel Extended Potential Field Controller for Use on Aerial Robots. The 12th Conference on Automation Science and Engineering (CASE), pages 1-6, August 21-24, 2016, Dallas, Texas, USA.
  • T. T. Han, H. M. La, B. H. Dinh. Flocking of Mobile Robots by Bounded Feedback. The 12th Conference on Automation Science and Engineering (CASE), pages 1-6, August 21-24, 2016, Dallas, Texas, USA.
  • A. C. Woods and H. M. La, Dynamic Target Tracking and Obstacle Avoidance using a Drone, the 11th International Symposium on Visual Computing, December 14-16, 2015 Las Vegas, Nevada, USA.
  • H. M. La,W. Sheng and J. Chen, Cooperative and active sensing in mobile sensor networks for scalar field mapping,  IEEE Transactions on Systems, Man and Cybernetics: Systems, pp.1-12, Vol. 45, No. 1, Jan. 2015. PDF
  • H. M. La, R. Lim and W. Sheng, Multi-robot cooperative learning for predator avoidanceIEEE Transactions on Control Systems Technology, pp.52-63, Vol. 23, No. 1, Jan. 2015. PDF
  • H. M. LaMulti-Robot Swarm for Cooperative Scalar Field Mapping,    in the Book “Handbook of Research on Design, Control, and Modeling of Swarm Robotics,” IGI Global, 2015, 383-395. Web. 14 Dec. 2015. DOI:10.4018/978-1-4666-9572-6.ch014
  • M. Jafari, S. Sengupta and H. M. La, Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV, the 11th International Symposium on Visual Computing, December 14-16, 2015 Las Vegas, Nevada, USA.
  • M. T. Nguyen, H. M. La and K. A. Teague, Compressive and Collaborative Mobile Sensing for Scalar Field Mapping in Robotic Networks, the  53rd Annual Allerton Conference on Communication, Control, and Computing, pages, Sept. 29-Oct. 2, 2015, Urbana-Champaign, Illinois, USA.
  • T. Nguyen, H. M. La and M. Jafari, On the Formation Control of a Multi Vehicle System, the ISSAT International Conference on Modeling of Complex Systems and Environments (MCSE), pages, June 8-10, 2015, Da Nang, Vietnam. 
  • T. Nguyen and H. M. La, Formation Control of Multiple Rectangular Agents with Limited Communication RangesThe 10th International Symposium on Visual Computing (ISVC), Dec. 8-10, 2014, Las Vegas, USA.