In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the Multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.

翻译：摘要：近年来，智能车辆的运动规划取得了显著进展。然而，作为典型的非结构化环境，露天采矿因其复杂的作业条件和不利的环境因素而受到的关注有限。本研究提出了一套面向露天矿无人运输的综合范式，包括仿真平台、测试基准以及可信赖且鲁棒的运动规划器。首先，我们提出了一种名为FusionPlanner的多任务运动规划算法，该算法基于多传感器融合方法，使自主矿用卡车能够适应无人运输中的横向和纵向控制任务。接着，我们开发了一个名为MiningNav的新颖基准，提供三种验证方法，用于评估经过良好训练的算法在露天矿运输道路上的可信赖性和鲁棒性。最后，我们介绍了并行采矿模拟器（PMS），这是一种专为露天采矿场景设计的新型高保真模拟器。PMS使操作者能够从单车控制和多车调度两个角度管理和控制露天矿运输。通过MiningNav在PMS中对FusionPlanner的性能进行了测试，实证结果表明，我们的规划器在碰撞次数和接管次数上显著减少。我们期待这一无人运输范式能使矿用卡车在持续不间断的无人运输中朝着可信赖性和鲁棒性迈出更近一步。