发表机构
Onyx Robotics; California Polytechnic State University（奥尼克斯机器人公司; 加州理工州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对欠驱动系统快速规划问题，提出可信多面体动作集方法，结合非线性保真与凸复用，规划效率与精度显著优于基线算法。
AI 中文摘要
欠驱动系统的凸运动规划面临挑战，因为其动态可行运动位于函数空间的轨迹流形上。本文在前期多面体动作集（Polytopic Action Sets, PAS）的公式基础上，提出一种为欠驱动及潜在非线性系统快速生成在线可信短 horizon 凸动作集的方法。围绕标称轨迹，我们构建局部有限维动作坐标，其中每个参数向量通过仿射轨迹映射编码完整的 nearby 运动，使避障和控制约束线性化。为与非线性动力学保持一致，我们引入动力学违反度量，并采用受 IRIS 启发的膨胀过程直接在动作空间中提取可信凸内近似。所得 PAS 是可复用的凸动作族，可通过线性规划查询与组合；PAS 引导的树扩展将节点视为组合可达族而非单条轨迹，结合局部非线性保真度与凸复用以实现更长 horizon 的规划。该规划器在杂乱平面场景中耗时数十毫秒，比运动学动力学快速探索随机树（kinodynamic RRT）基线快14至78倍；在非线性欠驱动基准上，其终端误差较采样和非线性规划（NLP）基线降低26%至86%。
英文摘要
Underactuated systems pose a challenge for convex motion planning because their dynamically feasible motions lie on a manifold of trajectories in function space. Building on our earlier formulation of polytopic action sets (PAS), this paper presents a method for rapidly generating, online, trusted convex sets of short-horizon actions for underactuated and potentially nonlinear systems. Around a nominal trajectory, we construct local finite-dimensional action coordinates in which each parameter vector encodes a complete nearby motion through an affine trajectory map, rendering collision-avoidance and control bounds linear. To remain consistent with the nonlinear dynamics, we introduce a dynamics-violation metric and extract a trusted convex inner approximation using an IRIS-inspired inflation procedure directly in action space. The resulting PAS are reusable convex families of actions that can be queried and composed with linear programs, and a PAS-guided tree expansion treats nodes as composed reachable families rather than single trajectories, coupling local nonlinear fidelity with convex reuse for longer-horizon planning. The planner solves cluttered planar scenes in tens of milliseconds (14-78x faster than a kinodynamic RRT baseline) and reduces terminal error on a nonlinear underactuated benchmark by 26-86% over sampling and NLP baselines.
CommentsAccepted for publication in IEEE Control Systems Letters (L-CSS); to be presented at the 2026 IEEE Conference on Decision and Control. Code available at https://github.com/akshay5312/paamp_underactuated
Journal refIEEE Control Systems Letters, vol. 10, pp. 2173-2178, 2026
DOI：10.1109/LCSYS.2026.3718036
