Publications
- Efficient Multi-Robot Search for a Moving Target
G. Hollinger, S. Singh, J. Djugash, and A. Kehagias.
The International Journal of Robotics Research, vol. 28, no. 2, pp. 201-219, 2009. [Abstract]We present an algorithm for locating a non-adversarial moving target with multiple robotic searchers. We give a detailed analysis of the benefit of finite-horizon search with implicit coordination in the efficient search domain. We demonstrate the performance of our algorithm using both simulated data and data from ultra-wideband ranging radios.
- Anytime Guaranteed Search using Spanning Trees
G. Hollinger, A. Kehagias, S. Singh, D. Ferguson, and S. Srinivasa.
Carnegie Mellon University Robotics Institute Technical Report, CMU-RI-TR-08-36, Aug. 2008. [Abstract]
Download: pdf [425 KB] copyrightedWe present an anytime algorithm for clearing an environment of an adversarial target with a team of multiple searchers. The algorithm uses spanning trees of the environment to guide search, and it improves performance as more trees are examined. This algorithm is demonstrated on two complex graphs derived from physical environments, and several methods for generating candidate spanning trees are compared.
- Coordinated Search in Cluttered Environments Using Range from Multiple Robots
G. Hollinger, J. Djugash, and S. Singh
Sixth International Conference on Field and Service Robotics, July, 2007. [Abstract]
Download: pdf [402 KB] copyrightedWe examine the problem of unified estimation and control in the multi-robot coordinated search domain. We extend our previous coordinated search framework to include noisy measurements from ranging radios. We show both simulated results and results in a laboratory environment with a Pioneer robot.
- Probabilistic Strategies for Pursuit in Cluttered Environments with Multiple Robots
G. Hollinger, A. Kehagias, and S. Singh
IEEE International Conference on Robotics and Automation, April, 2007. [Abstract]
Download: pdf [264 KB] copyrightedWe discuss a framework for coordinating multiple robots to efficiently search for a non-adversarial target in cluttered environments. We use finite-horizon path search with a cost heuristic along with decoupled path planning to achieve high-performance and scalable algorithms in this domain. We show simulated results to verify the performance of our framework.