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Direction-dependent optimal path planning for autonomous vehicles

机译:自动驾驶汽车的方向相关最佳路径规划

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摘要

The optimal path planning problem is considered for rovers. Tip-over risk is accurately modelled using direction dependence. In the previous direction-independent model, the value function was approximated using the Fast Marching Method (FMM). The risk was not accurately modelled. Solar energy is considered here for the first time. Minimizing path length, obstacle avoidance and soil risk are also considered. For a direction-dependent model, the value function in the optimal path planning problem can be approximated accurately using the Ordered Upwind Method (OUM)-but not FMK The value function is used to synthesize the optimal control, which is shown to have no local minima. A novel algorithmic improvement, OUM-BD over the OUM to include a bi-directional search is presented. The OUM-BD is slightly slower than the FMM, but can accurately solve a larger class of problems. The OUM-BD is faster than the existing OUM, an optimal hi-directional RRT path planner (Bi-RRT*), and a genetic algorithm (GA) path planner in terms of time, and outperforms both the GA and Bi-RRT* planner in cost in tested examples. (C) 2015 Elsevier B.V. All rights reserved.
机译:对于流动站,考虑了最佳路径规划问题。倾翻风险可使用方向依赖性进行精确建模。在以前的方向独立模型中,使用快速行进方法(FMM)来近似值函数。风险未正确建模。太阳能是第一次在这里考虑。还应考虑最小化路径长度,避开障碍物和土壤风险。对于方向相关的模型,可以使用有序迎风方法(OUM)精确地逼近最优路径规划问题中的值函数,但不能使用FMK来近似。值函数用于综合最优控制,这表明没有局部控制极小值。提出了一种新颖的算法改进,即OUM-BD优于OUM,包括双向搜索。 OUM-BD的速度比FMM稍慢,但是可以准确地解决更大的问题。 OUM-BD比现有的OUM更快,具有最佳的双向RRT路径规划器(Bi-RRT *)和遗传算法(GA)路径规划器,在时间上均胜过GA和Bi-RRT *测试示例中的成本规划师。 (C)2015 Elsevier B.V.保留所有权利。

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