Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach
Hamza Ahmed Durrani, Rafay Suleman Durrani
TLDR
This paper introduces SRAH, an LLM-inspired A* planner for risk-aware robot navigation in dynamic environments, showing improved success rates.
Key contributions
- SRAH planner integrates LLM-inspired semantic cost functions into A* search for risk-aware navigation.
- Implements closed-loop replanning to adapt to dynamic obstacles in real-time.
- Achieves 62.0% success, outperforming BFS (56.5%) and Greedy (4.0%) in dynamic environments.
- Shows semantic cost shaping consistently improves navigation safety and robustness across varying difficulties.
Why it matters
This paper is significant because it demonstrates how lightweight, LLM-inspired heuristics can substantially improve the safety and robustness of autonomous robot navigation in dynamic environments. It provides a practical approach to integrate semantic understanding into classical planning, leading to more reliable robotic systems.
Original Abstract
The integration of Large Language Model (LLM) reasoning principles into classical robot path planning represents a rapidly emerging research direction. In this paper, we propose a Semantic Risk-Aware Heuristic (SRAH) planner that encodes LLM-inspired cost functions penalising geometrically cluttered or high-risk zones into an A$^*$ search framework, augmented with closed-loop replanning upon dynamic obstacle detection. We evaluate SRAH against two established baselines Breadth-First Search (BFS) with replanning and a Greedy heuristic without replanning across 200 randomised trials in a $15{\times}15$ grid-world with 20\% static obstacle density and stochastic dynamic obstacles. SRAH achieves a task success rate of 62.0\%, outperforming BFS (56.5\%) by 9.7\% relative improvement and Greedy (4.0\%) by a large margin. We further analyse the trade-off between planning overhead, path efficiency, and failure-recovery count, and demonstrate via an obstacle-density ablation that semantic cost shaping consistently improves navigation across environments of varying difficulty. Our results suggest that even lightweight, LLM-inspired heuristics provide measurable safety and robustness gains for autonomous robot navigation.
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