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arXiv AI By Eran Iceland, Alexander Tuisov, Oren Gal, Ariel Barel, Alfred M. Bruckstein

Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods

Read the original on arXiv AI →

The paper compares classical motion planners with learning-based methods for real-time navigation in environments with dynamic hazards, using a unified benchmark that includes identical settings and metrics. In deterministic scenarios, classical planners achieve near-perfect success and high-quality paths, though they can incur significant planning time. When obstacle dynamics are stochastic, classical planners become highly budget-sensitive, whereas PPO-trained policies consistently deliver better latency, success rates, and path quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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