Hybrid Path Planning for Industrial Autonomous Vehicles: Integrating Neural Heuristics with Energy-Constrained Search

Lijun Xiao1, Xinwu Jian1, Zijian Wang1, Sisi Zhou2, Kuanching Li1

  1. School of Computer Science and Engineering, Hunan University of Science and Technology
    Xiangtan 411201, China
    ljxiao@hnust.edu.cn, 1435115512@qq.com, 19555725865@163.com,
    aliric@hnust.edu.cn (corresponding author)
  2. School of Computer Science and Engineering, Hunan Women’s University
    Changsha 410004, China
    sisizhou@mail.hnust.edu.cn (corresponding author)

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 NeuroPathNet-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.

Key words

Industrial Autonomous Vehicles, Energy-Constrained Path Planning, Neural Heuristic, Resource-Constrained Search, Multimodal Fusion, Bounded-Suboptimal Search

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS260425038X

Publication information

Volume 23, Issue 4 (September 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Xiao, L., Jian, X., Wang, Z., Zhou, S., Li, K.: Hybrid Path Planning for Industrial Autonomous Vehicles: Integrating Neural Heuristics with Energy-Constrained Search. Computer Science and Information Systems, 23(4) (2026). https://doi.org/10.2298/CSIS260425038X