Planning module is an essential component of intelligent vehicle study. In this paper, we address the risk-aware planning problem of UGVs through a global-local planning framework which seamlessly integrates risk assessment methods. In particular, a global planning algorithm named Coarse2fine A* is proposed, which incorporates a potential field approach to enhance the safety of the planning results while ensuring the efficiency of the algorithm. A deterministic sampling method for local planning is leveraged and modified to suit off-road environment. It also integrates a risk assessment model to emphasize the avoidance of local risks. The performance of the algorithm is demonstrated through simulation experiments by comparing it with baseline algorithms, where the results of Coarse2fine A* are shown to be approximately 30% safer than those of the baseline algorithms. The practicality and effectiveness of the proposed planning framework are validated by deploying it on a real-world system consisting of a control center and a practical UGV platform.

翻译：规划模块是智能车辆研究的重要组成部分。本文通过一个无缝集成风险评估方法的全局-局部规划框架，解决了无人地面车辆的风险感知规划问题。具体地，提出了一种名为粗到细A*的全局规划算法，该算法融合势场法以增强规划结果的安全性，同时保证算法效率。针对越野环境，改进了一种用于局部规划的确定性采样方法，并集成风险评估模型以强调局部风险的规避。通过仿真实验与基线算法对比，验证了算法性能：粗到细A*的结果比基线算法安全性提升约30%。通过在实际系统（包含控制中心与实用化无人地面车辆平台）上的部署，验证了所提规划框架的实用性与有效性。