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Kinodynamic Planning in the Configuration Space via Admissible Velocity Propagation

Abstract

We propose a method that enables kinodynamic planning in the configuration space (of dimension n) instead of the state space (of dimension 2n), thereby potentially cutting down the complexity of usual kinodynamic planning algorithms by an exponential factor. At the heart of this method is a new technique -- called Admissible Velocity Propagation (AVP) -- which, given a path in the configuration space and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path while respecting the system kinodynamic constraints. Combining this technique with usual sampling-based methods gives rise to a family of new motion planners that can appropriately handle kinodynamic constraints while avoiding the complexity explosion and, to some extent, the conceptual difficulties associated with a move to the state space.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Mathematics & Optimization
🧭 Keyword Pioneer — velocity propagation
🐣 Hot Topic Early Bird — motion planning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio