Machine Learning based Fixed-Time Optimal Path Generation

US Patent App. 16/222, 2019

Michael Yip, Mayur Bency, Ahmed Qureshi

Publisher Link: http://patents.google.com/patent/US20190184561A1/en

Abstract: Systems and methods are provided that introduce an improved way of producing fast and optimal motion plans by using Recurrent Neural Networks (RNN) to determine end-to-end trajectories in an iterative manner. By using an RNN in this way and offloading expensive computation towards offline learning, a network is developed that implicitly generates optimal motion plans with minimal loss in performance in a compact form. This method generates near optimal paths in a single, iterative, end-to-end roll-out that that has effectively fixed-time execution regardless of the configuration space complexity. Thus, the method results in fast, consistent, and optimal trajectories that outperform popular motion planning strategies in generating motion plans.

Yip et al. (2019) Machine Learning based Fixed-Time Optimal Path Generation, US Patent App. 16/222.