FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion
Siyu Teng, Luxi Li, Yuchen Li, Xuemin Hu, Lingxi Li, Yunfeng Ai, Long, Chen

TL;DR
FusionPlanner is a multi-task motion planning algorithm utilizing multi-sensor fusion for autonomous mining trucks, validated in a new high-fidelity simulator and benchmark, significantly improving safety and robustness in open-pit mining environments.
Contribution
The paper introduces FusionPlanner, a novel multi-task motion planning algorithm for mining trucks, along with a new benchmark MiningNav and a high-fidelity simulator PMS for open-pit mining scenarios.
Findings
FusionPlanner reduces collisions and takeovers significantly.
MiningNav effectively evaluates algorithm robustness.
PMS provides realistic simulation for open-pit mining transportation.
Abstract
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. 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…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robot Manipulation and Learning
