Co-ordinated Tracking and Planning Using Air and Ground Vehicles

The MAV ’08 competition in Agra, India focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. Executing this mission required addressing a number of technical challenges. The first such technical challenge was the design and operation of a micro air vehicle (MAV) capable of flying the necessary distance and carrying a sensor payload for localizing the hostages. The second technical challenge was the design and implementation of vision and state estimation algorithms to detect and track ground adversaries guarding the hostages. The third technical challenge was the design and implementation of robust planning algorithms that could co-ordinate with the MAV state estimates and generate tactical motion plans for ground vehicles to reach the hostage location without detection by the ground adversaries.

[1]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Hugh F. Durrant-Whyte,et al.  Recursive Bayesian search-and-tracking using coordinated uavs for lost targets , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[3]  Fatih Murat Porikli,et al.  Achieving real-time object detection and tracking under extreme conditions , 2006, Journal of Real-Time Image Processing.

[4]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Thierry Fraichard,et al.  Trajectory planning in a dynamic workspace: a 'state-time space' approach , 1998, Adv. Robotics.

[6]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Gerd Hirzinger,et al.  Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[8]  Randal W. Beard,et al.  Target Acquisition, Localization, and Surveillance Using a Fixed-Wing Mini-UAV and Gimbaled Camera , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[9]  Sai-Ming Li,et al.  Forest fire monitoring with multiple small UAVs , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Claire J. Tomlin,et al.  Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment , 2007 .

[11]  Thierry Siméon,et al.  A PRM-based motion planner for dynamically changing environments , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[12]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[13]  Roland Siegwart,et al.  Design and Control of an Indoor Coaxial Helicopter , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[15]  Ben Tse,et al.  Autonomous Inverted Helicopter Flight via Reinforcement Learning , 2004, ISER.

[16]  J. Karl Hedrick,et al.  A multiple UAV system for vision-based search and localization , 2008, 2008 American Control Conference.

[17]  David Silver,et al.  Cooperative Pathfinding , 2005, AIIDE.

[18]  Mark H. Overmars,et al.  Roadmap-based motion planning in dynamic environments , 2004, IEEE Transactions on Robotics.