RoadMap -Monte Carlo (PRM-MC) motion planner developed under the general methodology of PRM. For a given robot, PRM planners generally need to sample and connect a large number of robot configurations in order to build a madmap that reflects the properties (such as the.connectivity or energy landscape) of the robot configuration space. The proposed PRM-MC planner uses Monte Carlo simulation lo generate and connect neighboring mhot configurations and uses PRM local planners to connect the connected components generated from MC simulation. This strategy follows the random sampling principle of PRM that leads to the prohahilistic completeness of the PRM-type randomized plannen, while exploring the continuity property of motion planning constraints to improve the computation efficiency and roadmap quality. We apply the PRM-MC approach to closed chain motion planning in this paper. Our current planner uses roiation pivots as attempted Monte Carlo moves for 3D dosed chains with spherical joints. Pivot motions are developed as an efficient way lo deform closed chains withont violating the closure constraints, which have proved problematical for randomized appmaches. We will discuss how to identify feasible rotation pivots of kinematic chains and utilize them in PRM-MC planning. Our simulation results show that the PRM-MC closed chain planner can build madmaps with good connectivity and efficiently generate selfcollision-free closure configurations for closed chain systems with many links and multiple Imps. 07803-8232-3/04/$17.00 m004 IEEE
prm-mc combines prms and Monte Carlo simulations to guarantee closure constraints REF .
Hybrid probabilistic RoadMap - Monte Carlo motion planning for closed chain systems with spherical joints
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
Mathematics Computer Science