EA-SAC: ENERGY-AWARE: reinforcement learning for ski-steer local navigation
Energy-efficient local navigation on skid-steer mobile robots (SSMRs) in dense environments is constrained by a structural trade-off: the low-curvature, low-power manoeuvres that minimise locomotion energy conflict with the high-curvature, high-yaw-rate corrections required to weave through dense obstacle fields without colliding. Model-based planners do not resolve this tension but expose it as a cost knob, since weighting energy more heavily cuts consumption only by degrading performance as the scenes grow harder. We present LEAN, a deep reinforcement learning local planner that learns locomotion-efficient navigation from a calibrated energy signal while being model-free at inference, mapping observations directly to commands without solving any closed-form dynamic or power model online as model-based planners do. The energy signal enters the policy only as raw motor currents, and in place of dense goal-progress shaping the policy is guided by hindsight relabeling that keeps the reward path-shape-agnostic. Benchmarked on a 140-kg tracked SSMR against NMPC and EA-DWA variants that share the simulator’s identified dynamics and power model, raising the energy weight of either family lowers consumption only by sacrificing success, leaving the most aggressive energy-aware baselines up to 14 success points below LEAN at the hard tier. LEAN instead reduces energy by 10 to 14% in simulation and 19% on hardware over the energy-blind LEAN-Ablation at unchanged success rate and selects actions more than an order of magnitude faster than the model-based controllers, with consistent sim-to-real transfer in cluttered outdoor environments. The planner is named EA-SAC in the title and LEAN in the text, figures and tables; both names denote the same method.