Occupancy Based Map Searching Using Heterogeneous Teams of Autonomous Vehicles

In typical search missions, the environment and the targets are not stationary and previous observations become less reliable as time progresses. In addition, the search is often initiated with only a rough idea of target location. In this work we consider a strategy for searching using a team of heterogeneous autonomous vehicles. The team members maintain a world model which includes the estimate of possible target states. The issue of compelling agents to converge on targets and to search unexplored regions is formulated as a model predictive control problem. The world model is propagated in time and strategic decisions are made autonomously based on its prediction. Agents formulate control decisions by optimizing an objective function which allows for control and timing constraints. Individual agents in the team are coupled to one another through the centralized occupancy based map. This coupling, in combination with the outlined search strategy, leads to an e‐cient, autonomous, and cooperative search.

[1]  Marios M. Polycarpou,et al.  A cooperative search framework for distributed agents , 2001, Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206).

[2]  Hugh F. Durrant-Whyte,et al.  Process model, constraints, and the coordinated search strategy , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[3]  Christopher W. Lum.  A Single Agent Search of a Two Dimensional Space Using Probability Collectives and Convex Optimization , 2006 .

[4]  Anawat Pongpunwattana,et al.  Real-Time Planning for Multiple Autonomous Vehicles in Dynamic Uncertain Environments , 2004, J. Aerosp. Comput. Inf. Commun..

[5]  Christopher W. Lum,et al.  Autonomous Airborne Geomagnetic Surveying and Target Identiflcation , 2005 .

[6]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[7]  David H. Wolpert,et al.  A comparative study of probability collectives based multi-agent systems and genetic algorithms , 2005, GECCO '05.

[8]  Hugh Durrant-Whyte,et al.  Communication In General Decentralised Filters And The Coordinated Search Strategy , 2004 .

[9]  E. Fernandez-Gaucherand,et al.  Cooperative control for multiple autonomous UAV's searching for targets , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[10]  Hugh F. Durrant-Whyte,et al.  Coordinated decentralized search for a lost target in a Bayesian world , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).