Distributed computation of the knn graph for large high-dimensional point sets
Journal of Parallel and Distributed Computing
Motion planning in order to optimize the length and clearance applying a Hopfield neural network
Expert Systems with Applications: An International Journal
Hybrid systems: from verification to falsification by combining motion planning and discrete search
Formal Methods in System Design
Online world modeling and path planning for an unmanned helicopter
Autonomous Robots
On the performance of random linear projections for sampling-based motion planning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Hybrid systems: from verification to falsification
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Motion planning with dynamics by a synergistic combination of layers of planning
IEEE Transactions on Robotics
Algorithms and theory of computation handbook
Meshing and Simplification of High Resolution Urban Surface Data for UAV Path Planning
Journal of Intelligent and Robotic Systems
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
Survey on model-based manipulation planning of deformable objects
Robotics and Computer-Integrated Manufacturing
ACM SRC poster: from days to seconds: scalable parallel algorithm for motion planning
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
Sparse roadmap spanners for asymptotically near-optimal motion planning
International Journal of Robotics Research
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This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners.