GPU-Accelerated Optimization-Based Collision Avoidance
<|reference_start|>GPU-Accelerated Optimization-Based Collision Avoidance: This paper proposes a GPU-accelerated optimization framework for collision avoidance problems where the controlled objects and the obstacles can be modeled as the finite union of convex polyhedra. A novel collision avoidance constraint is proposed based on scale-based collision detection and the strong duality of convex optimization. Under this constraint, the high-dimensional non-convex optimization problems of collision avoidance can be decomposed into several low-dimensional quadratic programmings (QPs) following the paradigm of alternating direction method of multipliers (ADMM). Furthermore, these low-dimensional QPs can be solved parallel with GPUs, significantly reducing computational time. High-fidelity simulations are conducted to validate the proposed method's effectiveness and practicality.<|reference_end|>
@article{wu2024gpu-accelerated,
    title={GPU-Accelerated Optimization-Based Collision Avoidance},
    author={Zeming Wu, Zhuping Wang and Hao Zhang},
    journal={arXiv preprint arXiv:2406.07048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07048},
    primaryClass={cs.RO}
}