Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric

TL;DR

This paper presents UPP, a novel path planning algorithm that adaptively balances safety and optimality, with a new metric for evaluating this trade-off, demonstrating superior performance in simulations and real-world tests.

Contribution

Introduction of UPP, a graph-search algorithm that dynamically balances safety and optimality using adaptive heuristics and a new evaluation metric.

Findings

UPP achieves high safety-optimality trade-off scores in cluttered environments.

UPP maintains low path-length overhead and high success rate in simulations.

Hardware tests confirm practical benefits of UPP in real robot navigation.

Abstract

Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on real-time search progress, achieving provable suboptimality bounds while maintaining superior clearance. To enable rigorous evaluation, we introduce the OptiSafe index, a normalized metric that quantifies the trade-off between safety and optimality. Extensive evaluation across 10 environments shows that UPP achieves a 0.94 OptiSafe score in cluttered environments, compared with 0.22-0.85 for existing methods, with only 0.5-1% path-length overhead in simulation and a 100% success rate. Hardware validation on TurtleBot confirms practical advantages despite sim-to-real gaps.