Energy-Aware A* Path Planning for Indoor AGVs

This paper presents an energy-aware path planner for indoor autonomous guided vehicles, that maintains A*’s transparency while substituting geometric edge costs with laboratory-calibrated energy terms for rolling resistance, turn severity, near-wall operation and stop–go events. Implemented in RVC3-Python and evaluated across four scenarios of increasing complexity, the proposed algorithm delivers smoother, clearance-preserving routes and reduces total energy with only marginal path-length changes. The approach stands as a deployable, physically grounded solution for battery-constrained indoor robots.