This paper introduces the Smac Planner, an openly available search-based planning framework with multiple algorithm implementations including 2D-A*, Hybrid-A*, and State Lattice planners. This work is motivated by the lack of performant and available feasible planners for mobile and surface robotics research. This paper contains three main contributions. First, it briefly describes a minimal open-source software framework where search-based planners may be easily added. Further, this paper characterizes new variations on the feasible planners - dubbed Cost-Aware - specific to mobile roboticist's needs. This fills the gap of missing kinematically feasible implementations suitable for academic, extension, and deployed use. Finally, we provide baseline benchmarking against other standard planning frameworks. Smac Planner has further significance by becoming the standard open-source planning system within ROS 2's Nav2 framework which powers thousands of robots in research and industry.

翻译：本文介绍了Smac Planner，这是一个基于搜索的开源规划框架，包含多种算法实现，包括2D-A*、Hybrid-A*和状态晶格规划器。此项工作的动机在于移动与地面机器人研究中缺乏高性能且可用的可行规划器。本文包含三项主要贡献：首先，简要描述了一个最小化开源软件框架，便于添加基于搜索的规划器；其次，针对移动机器人学家的特定需求，提出了可行规划器的新变体——称为"成本感知"（Cost-Aware），填补了适用于学术、扩展和部署场景的运动学可行实现方案的空缺；最后，我们提供了与其他标准规划框架的基线基准测试。Smac Planner的进一步意义在于其已成为ROS 2的Nav2框架中的标准开源规划系统，驱动着研究和工业领域中的数千台机器人。