The accelerated deployment of service robots have spawned a number of algorithm variations to better handle real-world conditions. Many local trajectory planning techniques have been deployed on practical robot systems successfully. While most formulations of Dynamic Window Approach and Model Predictive Control can progress along paths and optimize for additional criteria, the use of pure path tracking algorithms is still commonplace. Decades later, Pure Pursuit and its variants continues to be one of the most commonly utilized classes of local trajectory planners. However, few Pure Pursuit variants have been proposed with schema for variable linear velocities—they either assume a constant velocity or fails to address the point at all. This paper presents a variant of Pure Pursuit designed with additional heuristics to regulate linear velocities, built atop the existing Adaptive variant. The Regulated Pure Pursuit algorithm makes incremental improvements on state of the art by adjusting linear velocities with particular focus on safety in constrained and partially observable spaces commonly negotiated by deployed robots. We present experiments with the Regulated Pure Pursuit algorithm on industrial-grade service robots. We also provide a high-quality reference implementation that is freely included ROS 2 Nav2 framework at https://github.com/ros-planning/navigation2 for fast evaluation.

服务机器人的加速部署催生了许多算法变体，以更好地处理现实世界的情况。许多局部轨迹规划技术已成功部署到实际机器人系统中。虽然动态窗口方法和模型预测控制的大多数公式都可以沿着路径前进并针对其他标准进行优化，但纯路径跟踪算法的使用仍然很普遍。几十年后，Pure Pursuit 及其变体仍然是最常用的局部轨迹规划器类别之一。然而，很少有人提出具有可变线性速度模式的 Pure Pursuit 变体——它们要么假设速度恒定，要么根本无法解决该点。本文介绍了 Pure Pursuit 的一种变体，设计有额外的启发式方法来调节线速度，建立在现有的自适应变体之上。这Regulated Pure Pursuit 算法通过调整线速度对现有技术进行逐步改进，特别关注部署机器人通常协商的受限和部分可观察空间中的安全性。我们在工业级服务机器人上展示了 Regulated Pure Pursuit 算法的实验。我们还在 https://github.com/ros-planning/navigation2 提供免费包含 ROS 2 Nav2 框架的高质量参考实现，用于快速评估。