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arXiv · 2507.09309

Informed Hybrid Zonotope-based Motion Planning Algorithm

Peng Xie·Johannes Betz·Amr Alanwar
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Abstract

Optimal path planning in nonconvex free spaces poses substantial computational challenges. A common approach formulates such problems as mixed-integer linear programs (MILPs); however, solving general MILPs is computationally intractable and severely limits scalability. To address these limitations, we propose HZ-MP, an informed Hybrid Zonotope-based Motion Planner, which decomposes the obstacle-free space and performs low-dimensional face sampling guided by an ellipsotope heuristic, thereby concentrating exploration on promising transition regions. This structured exploration mitigates the excessive wasted sampling that degrades existing informed planners in narrow-passage or enclosed-goal scenarios. We prove that HZ-MP is probabilistically complete and asymptotically optimal, and demonstrate empirically that it converges to high-quality trajectories within a small number of iterations.

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Peng Xie, Johannes Betz, Amr Alanwar. 2025-07-12. Informed Hybrid Zonotope-based Motion Planning Algorithm. https://arxiv.org/abs/2507.09309

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