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Autonomous Parking by Successive Convexification and Compound State Triggers

2020-10-11 09:25:17
Ali Boyali, Simon Thompson
arXiv_RO
         
Abstract

In this paper, we propose an algorithm for optimal generation of nonholonomic paths for planning parking maneuvers with a kinematic car model. We demonstrate the use of Successive Convexification algorithms (SCvx), which guarantee path feasibility and constraint satisfaction, for parking scenarios. In addition, we formulate obstacle avoidance with state-triggered constraints which enables the use of logical constraints in a continuous formulation of optimization problems. This paper contributes to the optimal nonholonomic path planning literature by demonstrating the use of SCvx and state-triggered constraints which allows the formulation of the parking problem as a single optimisation problem. The resulting algorithm can be used to plan constrained paths with cusp points in narrow parking environments.

Abstract (translated)

URL

https://arxiv.org/abs/2010.05201

PDF

https://arxiv.org/pdf/2010.05201.pdf

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