Autonomous trucks in busy port terminals must navigate narrow aisles, tight corners, and frequent interactions with multiple vehicles while maintaining both safety and efficiency. This paper presents a hierarchical multiagent navigation framework that integrates an enhanced grid‐based Theta ∗ global planner with obstacle inflation and clearance‐aware costs, an artificial potential field (APF)–based local controller augmented by lightweight neural correction, and a simple coordination protocol for resolving intertruck conflicts. We evaluate the approach in a high‐fidelity Unity digital twin of the Port of Oulu using two traffic scenes with three trucks executing simultaneous tasks. Experiments are repeated under identical initial conditions with independent random perturbations to capture run‐to‐run variability, and results are reported as the mean ± standard deviation. We compare the proposed Theta ∗ ‐based planner with a standard grid‐based A ∗ baseline and an 8‐neighborhood A ∗ variant under the same occupancy grid, obstacle inflation, and curvature constraints to isolate the impact of expanded action sets within the A ∗ framework. A greedy heuristic baseline is also included in the simpler scene, where it can complete scheduling. Across trucks, Theta ∗ achieves 43.0% lower travel time and 39.4% fewer avoidance events than A ∗ in the dense‐yard scene and 59.5% lower travel time and 91.4% fewer avoidance events in the gate–yard scene, while also improving a combined tracking‐accuracy index by 22.1% and 12.7%, respectively. Path‐tracking evaluation shows stable mean errors (average mean lateral deviation ≈ 0.40 m and mean heading error ≈ 1.69° across trucks), with transient peaks mainly occurring at high‐curvature segments, narrow‐clearance passages, and interaction‐driven maneuvers. We further include a time‐bounded scalability study by increasing the local fleet size to assess the coordination overhead under denser intertruck interactions. These results indicate that clearance‐aware any‐angle planning, together with neural‐tuned local avoidance and lightweight coordination, can improve both efficiency and execution quality for port–yard truck autonomy.
Liu et al. (Thu,) studied this question.