Abstract
Navigation among dynamic obstacles is a fundamental task in robotics that has been modeled in various ways. In Safe Interval Path Planning, location is discretized to a grid, time is continuous, future trajectories of obstacles are assumed known, and planning takes place offline. In this work, we define the Real-time Safe Interval Path Planning problem setting, in which the agent plans online and must issue its next action within a strict time bound. Unlike in classical realtime heuristic search, the cost-to-go in Real-time Safe Interval Path Planning is a function of time rather than a scalar. We present several algorithms for this setting and prove that they learn admissible heuristics. Empirical evaluation shows that the new methods perform better than classical approaches under a variety of conditions.
Original language | English |
---|---|
Pages (from-to) | 161-169 |
Number of pages | 9 |
Journal | The International Symposium on Combinatorial Search |
Volume | 17 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2024 |
Event | 17th International Symposium on Combinatorial Search, SoCS 2024 - Kananaskis, Canada Duration: 6 Jun 2024 → 8 Jun 2024 |
ASJC Scopus subject areas
- Computer Networks and Communications