Efficient Reliability-Based Path Planning of Off-Road Autonomous Ground Vehicles Through the Coupling of Surrogate Modeling and RRT*
Efficient Reliability-Based Path Planning of Off-Road Autonomous Ground Vehicles Through the Coupling of Surrogate Modeling and RRT*
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DOI：
10.1109/tits.2023.3296651
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发表时间：
2023-12
影响因子：
8.5
通讯作者：
Jianhua Yin;Zhen Hu;Z. Mourelatos;D. Gorsich;Amandeep Singh;Seth Tau
中科院分区：
文献类型：
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作者：
Jianhua Yin;Zhen Hu;Z. Mourelatos;D. Gorsich;Amandeep Singh;Seth Tau
基于可靠性的全局路径规划将可靠性约束纳入路径规划中，以保证越野自主地面车辆在不确定的越野环境下能够可靠运行。目前基于可靠性的两阶段路径规划方法将机动性预测的代理建模和全局路径规划分开进行，导致大量不必要的机动性仿真，使得该方法的计算成本很高。为了解决这一挑战，本研究提出了一种新的高效的基于可靠性的全局路径规划方法，称为ER-RRT*，该方法将自适应代理建模与快速探索随机树星（RRT*）算法相结合。首先，利用车辆机动性预测的代理模型来指导受机动性可靠性约束的随机树的探索；随后，利用探索树和可靠性评估为代理模型的细化提供机动性仿真信息。这些步骤是迭代实现的，因此通过自适应代理建模与全局路径规划的集成，大大减少了路径规划所需的移动性模拟。针对斜坡图的不确定性和变形地形的土壤性质，我们以一个案例来演示ER-RRT*，并将其与目前的两阶段方法进行比较。结果表明，ER-RRT*在计算时间和构建代理模型所需的迁移模拟次数方面都比现有方法效率高得多。此外，ER-RRT*识别的路径与使用两阶段法获得的路径在距离上的成本相当。
Reliability-based global path planning incorporates reliability constraints into path planning to ensure that off-road autonomous ground vehicles can operate reliably in uncertain off-road environments. Current two-stage reliability-based path planning methods involve separate stages for surrogate modeling of mobility prediction and global path planning, resulting in a large number of unnecessary mobility simulations that makes the approaches computationally expensive. To tackle this challenge, this work proposes a novel efficient reliability-based global path planning approach, named ER-RRT*, which couples adaptive surrogate modeling with the rapidly-exploring random tree star (RRT*) algorithm. Firstly, a surrogate model for vehicle mobility prediction is used to guide the exploration of random trees subject to a mobility reliability constraint. Subsequently, the exploration trees and reliability assessment are employed to inform mobility simulations for the surrogate model refinement. These steps are implemented iteratively and thereby drastically reducing the required mobility simulations for path planning through the integration of adaptive surrogate modeling with global path planning. With a focus on the uncertainty in the slope map and soil properties of deformable terrain, we demonstrate ER-RRT* using a case study and compare it with the current two-stage approach. The results show that ER-RRT* is much more efficient than the current method in both computational time and the required number of mobility simulations for surrogate model construction. In addition, the path identified by ER-RRT* exhibits a comparable cost in distance to its counterpart obtained using the two-stage method.