Hybrid path planning for industrial autonomous vehicles: Integrating neural heuristics with energy-constrained search

Abstract

Energy-aware path planning for industrial autonomous vehicles requires more than a geometric shortest path: terrain, payload, drivetrain efficiency, turning, and a hard task-level energy budget jointly affect feasibility. We present Neuro-PathNet-EC, a physics-aware constrained planner with learned multimodal guidance. The planner operates on heading-aware grid states and evaluates directed edge energy from friction and elevation. Exact Pareto labels enforce the hard energy budget, while a goal-conditioned ResNet-18 network fuses four raster channels with vehicle semantics and predicts an eight-channel dense cost-to-go map. The learned heuristic is used only for guidance; it is not assumed admissible or consistent. Exact Energy-A* obtains its guarantee from an analytic admissible anchor, and a FOCAL extension provides the bound C ≤ wC*. On a layout-disjoint synthetic industrial dataset, five training seeds achieve a test relative heuristic MAE of 0.0289 ± 0.0014 and Spearman correlation of 0.9963 ± 0.0004. Across 512 paired binding-budget queries per seed (2,560 seed-query runs), full multimodal neural-constrained search returns the exact constrained objective in every run while reducing expanded labels by 10.73% relative to Energy-A*. The semantic branch also reduces labels relative to an otherwise matched image-only model. FOCAL satisfies its declared bound in all evaluated cases, although its current correctness-first OPEN management does not improve runtime. The evaluation is based on synthetic layouts and does not constitute public-benchmark or real-vehicle validation.