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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 real-time 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.
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This content will become publicly available on August 17, 2026
Risk-Averse Autonomous Material Handling in Healthcare Systems
The safe internal transportation of hazardous materials within healthcare facilities is critical to mitigating risks to patients, staff, and visitors. This paper presents a risk-averse path planning framework for autonomously handling hazardous materials in healthcare systems. We model the indoor environment with grid-based obstacle and risk maps, where risk arises from pedestrian flow density and proximity to critical zones. Our novel risk-averse path planning approach integrates risk directly into each transition cost, thereby enabling more robust and secure path selection. We further improve efficiency through (i) a bidirectional variant that cuts search time and (ii) a post-optimization step that minimizes unnecessary heading changes while respecting a risk budget. We evaluated our framework on multiple simulated grid maps and compared it with established methods, measuring path length, average risk, and computational time. The results demonstrate that the proposed framework consistently generates safe and efficient paths while minimizing computational overhead.
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- Award ID(s):
- 2302834
- PAR ID:
- 10639950
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2161-8089
- Page Range / eLocation ID:
- 1050 to 1055
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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