Real-time Funnel Generation for Restricted Motion Planning
This work addresses safety and efficiency in autonomous motion planning, but it appears incremental as it builds on existing methods to reduce conservatism.
The paper tackles the problem of generating provably safe tracking error bounds for motion planners in autonomous systems, reducing over-conservatism by restricting planner behaviors using sum-of-squares programming, and demonstrates acceptable error bounds in case studies where previous methods failed.
In autonomous systems, a motion planner generates reference trajectories which are tracked by a low-level controller. For safe operation, the motion planner should account for inevitable controller tracking error when generating avoidance trajectories. In this article we present a method for generating provably safe tracking error bounds, while reducing over-conservatism that exists in existing methods. We achieve this goal by restricting possible behaviors for the motion planner. We provide an algebraic method based on sum-of-squares programming to define restrictions on the motion planner and find small bounds on the tracking error. We demonstrate our method on two case studies and show how we can integrate the method into already developed motion planning techniques. Results suggest that our method can provide acceptable tracking error wherein previous work were not applicable.