Method to optimize robot motion planning using deep learning
US11334085B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 24, 2021 |
| Grant date | May 17, 2022 |
| Priority date | — |
| Expiry date | May 24, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/40519
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems are provided for high-speed constrained motion planning. In one embodiment, a method includes computing, with a neural network trained on trajectories generated by a non-convex optimizer, a trajectory from one or more initial states of an autonomous system to one or more final states of the autonomous system, updating, with the non-convex optimizer, the trajectory according to kinematic limits and dynamic limits of the autonomous system to obtain a final trajectory, and automatically controlling the autonomous system from an initial state of the one or more initial states to a final state of the one or more final states according to the final trajectory. In this way, efficient and smooth trajectories can be rapidly computed for effective real-time control while accounting for obstacles and physical constraints of an autonomous system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.