Unified Path Planner with Adaptive Safety and Optimality
This addresses the problem of balancing path length and obstacle clearance for autonomous robots, but it is incremental as it builds on existing graph-search methods.
The paper tackles the trade-off between optimality and safety in autonomous robot path planning by introducing the Unified Path Planner (UPP), which achieves a high success rate with near-optimal paths and safety margins close to those of classical methods.
Path planning for autonomous robots presents a fundamental trade-off between optimality and safety. While conventional algorithms typically prioritize one of these objectives, we introduce the Unified Path Planner (UPP), a unified framework that simultaneously addresses both. UPP is a graph-search-based algorithm that employs a modified heuristic function incorporating a dynamic safety cost, enabling an adaptive balance between path length and obstacle clearance. We establish theoretical sub-optimality bounds for the planner and demonstrate that its safety-to-optimality ratio can be tuned via adjustable parameters, with a trade-off in computational complexity. Extensive simulations show that UPP achieves a high success rate, generating near-optimal paths with only a negligible increase in cost over traditional A*, while ensuring safety margins that closely approach those of the classical Voronoi planner. Finally, the practical efficacy of UPP is validated through a hardware implementation on a TurtleBot, confirming its ability to navigate cluttered environments by generating safe, sub-optimal paths.