A Fuzzy Logic-Based Adaptive Dynamic Window Approach for Path Planning of Automated Driving Mining Truck

Yu Lei, Yafei Wang, Shaoteng Wu, X. Gu, Xiaojun Qin
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引用次数: 6

Abstract

Automated Driving for open-pit mines has become a trend in recent years, and one of the most challenging scenario is parking path planning based on the demand of the excavator. In path planning field, dynamic window approach (DWA) is widely used. However, such method negalects expected direction at the destination, which makes it difficult to be implemented for mining trucks parking. To deal with this problem, we proposed a fuzzy logic-based adaptive DWA by considering truck kinematics. Firstly, an improved DWA considering obstacle avoidance is designing by taking Ackermann steering constraint into account. In the approach, we design a novel objective function to regulate the truck direction. Secondly, the detail of fuzzy logic is presented to guarantee the robustness against different situations. Simulations performed with the fuzzy logic-based adaptive DWA show that the mining truck can drive to the destination in high direction accuracy in different scenarios.
基于模糊逻辑的矿用自动驾驶卡车路径规划自适应动态窗口方法
近年来,露天矿山的自动驾驶已成为一种趋势,其中最具挑战性的场景之一是基于挖掘机需求的停车路径规划。在路径规划领域,动态窗口法(DWA)被广泛应用。然而,该方法忽略了目的地的期望方向,使得采矿卡车停车难以实现。为了解决这一问题,我们提出了一种基于模糊逻辑的考虑卡车运动学的自适应DWA。首先,考虑Ackermann转向约束,设计了一种考虑避障的改进DWA;在该方法中,我们设计了一个新的目标函数来调节卡车的方向。其次,给出了模糊逻辑的细节,以保证系统在不同情况下的鲁棒性。仿真结果表明,基于模糊逻辑的自适应DWA在不同场景下均能以较高的方向精度行驶到目的地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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