Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Exploring artificial intelligence in the new millennium
Locating moving entities in indoor environments with teams of mobile robots
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visibility-based pursuit-evasion with limited field of view
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Exploiting a meeting channel to interconnect mobile robots
Journal of Network and Computer Applications
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We describe a novel method whereby a particle filter is used to create a potential field for robot control without prior clustering. We show an application of this technique to control a team of mobile robots to cooperatively locate and track a moving target. The particle filter models a probability distribution over the estimated location of the target, providing robust tracking despite frequent target occlusion. This method extends previous work in particle-filter-based tracking in two important ways. First, the particle cloud is never clustered to find a single estimate of target location. Instead, robot motion is guided by a potential field generated directly from the particle cloud. Secondly, effective coordinated multi-robot searching and tracking can be achieved by simply assigning a subset of the particles to each robot. Simulation trials and real robot experiments demonstrate the method successfully locating and tracking targets, and experiments show that multiple coordinated robots outperform a similar but uncoordinated team.