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Showing 1 to 5 of 5 for “"Sampling-based Planning"”.
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Learning Probabilistic Generative Models For Fast Sampling-Based Planning
… and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision …
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Sampling and Searching Methods for Practical Motion Planning Algorithms
In its original conception, the motion planning problem considered the search of a robot path from an initial to a goal configuration. The study of motion planning has advanced significantly in recent years, in large part due to the development of highly successful sampling and searching …
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Efficient Learning and Inference for High-dimensional Lagrangian Systems
… of these methods to high-dimensional motion planning. Algorithms are given to learn and exploit the struc- ture of holonomic motion planning problems effectively via spectral analysis and iterative dynamic programming, admitting solutions to problems of unprecedented dimension com- pared to …
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Proving infeasibility in motion planning
Motion planning is a fundamental problem in robotics that has received a lot of attention. The motion planning problem is NP-complete, but previous works in motion planning have been very successful in efficiently finding a valid path in motion planning problems. One class of solutions for solving …
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Rapid, assured planning for safe operation of integrated power, propulsion, and thermal systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms