Sampling-based motion planning algorithms are widely used in robotics because they are very effective in high-dimensional spaces. However, the success rate and quality of the solutions are determined by an adequate selection of their parameters such as the distance between states, the local planner, and the sampling distribution. For robots with large configuration spaces or dynamic restrictions, selecting these parameters is a challenging task. This paper proposes a method for improving the performance to a set of the most popular sampling-based algorithms, the Rapidly-exploring Random Trees (RRTs) by adjusting the sampling method. The idea is to replace the uniform probability density function (U-PDF) with a custom distribution (C-PDF) learned from previously successful queries in similar tasks. With a few samples, our method builds a custom distribution that allows the RRT to grow to promising states that will lead to a solution. We tested our method in several autonomous driving tasks such as parking maneuvers, obstacle clearance and under narrow passages scenarios. The results show that the proposed method outperforms the original RRT and several improved versions in terms of success rate, tree density and computation time. In addition, the proposed method requires a relatively small set of examples, unlike current deep learning techniques that require a vast amount of examples.
翻译:以抽样为基础的运动规划算法在高维空间非常有效,因此在机器人中广泛使用。然而,解决方案的成功率和质量是通过适当选择参数来确定的,例如各州之间的距离、当地规划员和抽样分布。对于配置空间或动态限制较大的机器人来说,选择这些参数是一项艰巨的任务。本文件提出一种方法,通过调整取样方法,改进一套最受欢迎的抽样算法的性能,即快速探索随机树(RRTs)的性能,从而调整取样方法。其想法是用从以前成功查询类似任务中汲取的自定义分布(C-PDF)取代统一概率密度函数(U-PDF)。我们的方法通过少量样本建立自定义分布,使RRT能够成长到有希望的状态,从而找到解决办法。我们在若干自主驾驶任务中测试了我们的方法,例如停车操作、清除障碍和在狭窄通道设想下进行。结果显示,拟议的方法在成功率、树密度和计算时间方面优于原始RRT和若干经改进的小型版本。我们的方法需要一种相对不同的深度学习方法。