An Information Roadmap Method for Robotic Sensor Path Planning
Journal of Intelligent and Robotic Systems
Vision-based reactive multiple obstacle avoidance for micro air vehicles
ACC'09 Proceedings of the 2009 conference on American Control Conference
Detecting narrow passages in configuration spaces via spectra of probabilistic roadmaps
Proceedings of the 2010 ACM Symposium on Applied Computing
Reactive Path Planning for Micro Air Vehicles Using Bearing-only Measurements
Journal of Intelligent and Robotic Systems
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Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.