Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki,P. Svestka,Jean-Claude Latombe,Mark H. Overmars +3 more
- Vol. 12, Iss: 4, pp 566-580
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TLDR
Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).Abstract:
A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).read more
Citations
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Proceedings ArticleDOI
FIRM: Feedback controller-based information-state roadmap - A framework for motion planning under uncertainty
TL;DR: This paper generalizes the Probabilistic RoadMap framework to obtain a Feedback controller-based Information-state RoadMap (FIRM) that takes into account motion and sensing uncertainty in planning and shows how obstacles can be rigorously incorporated into planning on FIRM.
Journal ArticleDOI
Algorithms for Computing Numerical Optimal Feedback Motion Strategies
TL;DR: This work improves classical optimal control techniques for problems of interest to the authors by introducing a simplicial complex representation and proposing a novel interpolation scheme that reduces a key bottleneck in the techniques from O(2n) running time to O(n lg n), in which n is the state space dimension.
Journal ArticleDOI
Using Motion Planning for Knot Untangling
Andrew M. Ladd,Lydia E. Kavraki +1 more
TL;DR: A probabilistic planner that is capable of untangling knots described by over 400 variables is developed and tested on known difficult benchmarks and untangled them more quickly than has been achieved with minimization in the literature.
Journal ArticleDOI
Pseudospectral Motion Planning for Autonomous Vehicles
TL;DR: This work considers the problem of generating optimal trajectories for generic autonomous vehicles and applies pseudospectral methods to develop motion planning algorithms for autonomous vehicles characterized by nonlinear dynamical constraints, an obstacle-cluttered environment, and a need to generate solutions in real time.
Journal ArticleDOI
A hierarchical path planning approach based on A* and least-squares policy iteration for mobile robots
TL;DR: A novel hierarchical path planning approach for mobile robot navigation in complex environments using an approximate policy iteration algorithm called least-squares policy iteration (LSPI) that can generate smooth trajectories under kinematic constraints of the robot.
References
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Journal ArticleDOI
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