Abstract
We consider automated vehicles operation in constrained environments, i.e. the automated parking (AP). The core of AP is formulated as a path planning problem, and Rapidly-exploring Randomized Tree (RRT) algorithm is adopted. To improve the baseline RRT, we propose several algorithmic tweaks, i.e. reversed RRT tree growth, direct tree branch connection using Reeds-Shepp curves, and RRT seeds biasing via regulated parking space/vehicle knowledge. We prove that under these tweaks the algorithm is complete and feasible. We then examine its performance (time, success rate, convergence to the optimal path) and scalability (to different parking spaces/vehicles) via batched simulations. We also test it using a real vehicle in a realistic parking environment. The proposed solution presents itself more applicable when compared with other baseline algorithms.
Highlights
INTRODUCTIONIn [22] the authors followed the new thrust force of Deep Learning and adopted expert knowledge-trained conditional variational autoencoder (CVAE) in biasing the seeds
We propose several algorithmic tweaks to the baseline Rapidly-exploring Randomized Tree (RRT), i.e., seeds biasing using the expert parking knowledge in different parking spaces, Reeds-Shepp curves directly connecting the tree node and goal pose, and reversed tree growth that originates from the parking pose
In this paper we propose an unified solution for the automated vehicles parking problem
Summary
In [22] the authors followed the new thrust force of Deep Learning and adopted expert knowledge-trained conditional variational autoencoder (CVAE) in biasing the seeds Similar to these researches, we summarize the optimal parking paths that originate from random start poses in different parking spaces. We propose several algorithmic tweaks to the baseline RRT, i.e., seeds biasing using the expert parking knowledge in different parking spaces, Reeds-Shepp curves directly connecting the tree node and goal pose, and reversed tree growth that originates from the parking pose. Scalability: Lastly, we hope the above claims hold for all parking spaces (χ and xgoal at different angles/sizes), different vehicles, and any potential start poses (xstart ∈ χfree), in both simulation and real environments. We link each two consecutive nodes via the tree branches, which is the navigated path in each step of the tree expansion
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