Online Reachability Analysis and Space Convexification for Autonomous Racing

Sergiy Bogomolov
(Newcastle University)
Taylor T. Johnson
(Vanderbilt University)
Diego Manzanas Lopez
(Vanderbilt University)
Patrick Musau
(Vanderbilt University)
Paulius Stankaitis
(Newcastle University)

This paper presents an optimisation-based approach for an obstacle avoidance problem within an autonomous vehicle racing context. Our control regime leverages online reachability analysis and sensor data to compute the maximal safe traversable region that an agent can traverse within the environment. The idea is to first compute a non-convex safe region, which then can be convexified via a novel coupled separating hyperplane algorithm. This derived safe area is then used to formulate a nonlinear model-predictive control problem that seeks to find an optimal and safe driving trajectory. We evaluate the proposed approach through a series of diverse experiments and assess the runtime requirements of our proposed approach through an analysis of the effects of a set of varying optimisation objectives for generating these coupled hyperplanes.

In Marie Farrell, Matt Luckcuck, Mario Gleirscher and Maike Schwammberger: Proceedings Fifth International Workshop on Formal Methods for Autonomous Systems (FMAS 2023), Leiden, The Netherlands, 15th and 16th of November 2023, Electronic Proceedings in Theoretical Computer Science 395, pp. 95–112.
Published: 15th November 2023.

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