SRAH：受LLM启发的语义风险感知启发式规划器

This post introduces SRAH, a heuristic path planner integrating LLM reasoning principles into classic A* search. Key features include semantic risk cost functions (penalizing high-risk areas) and a closed-loop replanning mechanism. In dynamic environments, it achieves a 62% task success rate—9.7% higher than traditional BFS methods.

Robot path planning faces critical issues in dynamic environments:

• Dynamic environment特殊性: Presence of moving obstacles (pedestrians, vehicles) with unpredictable trajectories.
• Traditional method limitations:
  • Pure geometric planning ignores semantic info (e.g., crowd-dense zones).
  • Simple heuristics (Euclidean/manhattan distance) fail to capture complex risks.
  • Open-loop planning is fragile to environment changes.

SRAH adapts LLM's reasoning (semantic understanding, risk awareness, context adaptation) into classic A*:

1. Semantic Risk Heuristic: $f(n) = g(n) + h(n) + α·risk(n)$, where risk(n) includes geometric crowding, high-risk zones (stairs, wet floors), and dynamic risk accumulation.
2. A Integration*: Maintains open/closed lists and neighbor expansion while using the new heuristic.
3. Closed-loop Replanning: Monitors environment changes, triggers replanning when needed, and smooths path transitions.

Experiments in 15×15 grid worlds (20% static obstacles, random dynamic obstacles, 200 trials):

• Baselines: BFS+replanning, greedy (no replanning).
• Key Results:
  • SRAH success rate:62% (BFS:56.5% → +9.7%, greedy:4% → huge gain).
  • Overhead:15-20% more per node expansion but fewer nodes overall.
  • Path efficiency:5-10% longer than shortest path but safer.
• Ablation: SRAH outperforms baselines in medium/high obstacle density (20-30%).

SRAH's key contributions:

1. Lightweight LLM-inspired: Encodes LLM principles into classic algorithms (no neural nets → efficient, interpretable, deployable on embedded devices).
2. Classic-modern fusion: Combines A*'s efficiency with LLM's semantic/risk awareness.
3. Safety impact: Improves reliability in safety-critical applications (autonomous driving, service robots).

Current Limitations:

• Simplified grid world (vs continuous real environment).
• Hand-designed risk functions (needs domain knowledge).
• Assumes random dynamic obstacle movement (real-world behavior is complex).

Future Research:

• Extend to continuous space.
• Learn risk functions via imitation/reinforcement learning.
• Support multi-robot collaboration.
• Validate on physical robots.