Path planning is a key component in mobile robotics. A wide range of path planning algorithms exist, but few attempts have been made to benchmark the algorithms holistically or unify their interface. Moreover, with the recent advances in deep neural networks, there is an urgent need to facilitate the development and benchmarking of such learning-based planning algorithms. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learned 2D and 3D path planning algorithms, while offering support for Robot Oper-ating System (ROS). Many existing path planning algorithms are supported; e.g. A*, wavefront, rapidly-exploring random tree, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. We demonstrate the benchmarking capability of PathBench by comparing implemented classical and learned algorithms for metrics, such as path length, success rate, computational time and path deviation. These evaluations are done on built-in PathBench maps and external path planning environments from video games and real world databases. PathBench is open source.
翻译:路径规划是移动机器人中的一个关键组成部分。 存在一系列广泛的路径规划算法,但很少尝试用整体或统一其界面来基准算法。 此外,随着深神经网络的近期进步,迫切需要为这种基于学习的规划算法的发展和基准制定提供便利。 本文展示了“ 路北”,这是一个开发、可视化、培训、测试和基准制定现有和未来、传统和学习的2D和3D路径规划算法的平台,同时为机器人操作系统(ROS)提供支持。 许多现有的路径规划算法得到了支持;例如,A*、波浪、快速探索随机树、价值迭代网络、封闭路径规划网络;整合新的算法是容易和明确的。我们通过比较执行的古典和学习的测量算法(如路径长度、成功率、计算时间和路径偏差),展示了“ 路北” 的基准算法能力。 这些评价是在“ 路径” 内部地图和外部路径规划环境上进行的,来自视频游戏和真实世界数据库。 路径BathBench 是开放的来源。