In this work we present CppFlow - a novel and performant planner for the Cartesian Path Planning problem, which finds valid trajectories up to 129x faster than current methods, while also succeeding on more difficult problems where others fail. At the core of the proposed algorithm is the use of a learned, generative Inverse Kinematics solver, which is able to efficiently produce promising entire candidate solution trajectories on the GPU. Precise, valid solutions are then found through classical approaches such as differentiable programming, global search, and optimization. In combining approaches from these two paradigms we get the best of both worlds - efficient approximate solutions from generative AI which are made exact using the guarantees of traditional planning and optimization. We evaluate our system against other state of the art methods on a set of established baselines as well as new ones introduced in this work and find that our method significantly outperforms others in terms of the time to find a valid solution and planning success rate, and performs comparably in terms of trajectory length over time. The work is made open source and available for use upon acceptance.

翻译：本文提出CppFlow——一种针对笛卡尔路径规划问题的新型高效规划器，其寻找有效轨迹的速度比现有方法快129倍，并能在其他方法失效的更复杂问题上取得成功。该算法的核心在于采用学习型生成式逆运动学求解器，能够在GPU上高效生成完整的候选解轨迹。随后通过可微编程、全局搜索与优化等经典方法获得精确有效的解。通过融合这两种范式的优势，我们既获得了生成式AI提供的高效近似解，又通过传统规划与优化的理论保证使其精确化。我们在既有基准测试集及本文提出的新测试集上，将本系统与当前最先进方法进行对比评估，结果表明：本方法在寻找有效解的时间和规划成功率方面显著优于其他方法，在轨迹长度随时间变化方面表现相当。本工作将在录用后开源供公众使用。