12 research outputs found

    Coordinating robot motion, sensing, and control in plans. LDRD project final report

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    ADAPTIVE PROBABILISTIC ROADMAP CONSTRUCTION WITH MULTI-HEURISTIC LOCAL PLANNING

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    The motion planning problem means the computation of a collision-free motion for a movable object among obstacles from the given initial placement to the given end placement. Efficient motion planning methods have many applications in many fields, such as robotics, computer aided design, and pharmacology. The problem is known to be PSPACE-hard. Because of the computational complexity, practical applications often use heuristic or incomplete algorithms. Probabilistic roadmap is a probabilistically complete motion planning method that has been an object of intensive study over the past years. The method is known to be susceptible to the problem of “narrow passages”: Finding a motion that passes a narrow, winding tunnel can be very expensive. This thesis presents a probabilistic roadmap method that addresses the narrow passage problem with a local planner based on heuristic search. The algorithm is suitable for planning motions for rigid bodies and articulated robots including multirobot systems with many degrees-of-freedom. Variants of the algorithm are describe

    Human Assisted Assembly Processes

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    The Toggle Local Planner for sampling-based motion planning

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    CHOMP: Gradient optimization techniques for efficient motion planning

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    ROBOT PROGRAMMING AND TRAJECTORY PLANNING USING AUGMENTED REALITY

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    Ph.DDOCTOR OF PHILOSOPH
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