Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach

TL;DR

This paper introduces a Semantic Risk-Aware Heuristic (SRAH) planner inspired by LLM reasoning, which improves robotic navigation safety and success rates in dynamic environments by penalizing risky zones within an A* framework.

Contribution

The paper presents a novel LLM-inspired heuristic for robot path planning that enhances safety and robustness in dynamic environments, outperforming traditional methods.

Findings

SRAH achieves a 62.0% success rate, outperforming BFS and Greedy baselines.

Semantic cost shaping improves navigation success across various obstacle densities.

Lightweight LLM-inspired heuristics provide measurable safety benefits in robot navigation.

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.