一种用于快速避碰的任务空间滚动时域控制器

中文摘要 AI 辅助

机器人操纵器的实时避碰需要对意外障碍物运动快速反应并前瞻以避免陷入近期约束。全模型预测控制可提供前瞻，但在线成本可能随时域长度、模型保真度和有效几何约束数量快速增长。无时域的反应式方法计算效率高，但在动态杂波中可能目光短浅。本文提出一种任务空间滚动时域控制器，使用短的接触一致展开来生成满足内部非穿透约束的终端运动学参考，然后仅计算朝向该参考的平滑最小加速度过渡的第一个输入。从闭环逆运动学调节律开始，展开通过在膨胀凸机器人和障碍物几何形状上运行的迭代动力学求解器进行，使得机器人 - 障碍物接触、动态障碍物运动和自碰撞可塑造终端参考而无需完全约束轨迹优化。我们分析了非接触激活的闭环并在标准正则性假设下展示了局部指数任务空间调节。对于在展开内激活的接触，我们表征了相应的离散更新并界定了移动障碍物对常规操作集的影响。在40自由度多链系统上的仿真表明，中间时域平衡了预期、响应性和计算成本。在6自由度平台上的硬件实验证明了在没有精确惯性参数估计的情况下一致的仿真到实际行为，并且与动态优化框架和模型预测控制（MPC）基线的比较表明，在动态杂波中成功率提高，同时在测试范围内保持与实时执行兼容的求解时间。

英文摘要

Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion and lookahead to avoid becoming trapped by near-future constraints. Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints. Conversely, horizon-free reactive methods are computationally efficient but can be short-sighted in dynamic clutter. We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference satisfying internal non-penetration constraints, then computes only the first input of a smooth minimum-acceleration transition toward that reference. Starting from a closed-loop inverse-kinematics regulation law, the rollout is performed with an iterative dynamics solver operating on inflated convex robot and obstacle geometries, so that robot-obstacle contacts, dynamic obstacle motion, and self-collisions can shape the terminal reference without requiring full constrained trajectory optimization. We analyze the contact-inactive closed loop and show local exponential task-space regulation under standard regularity assumptions. For contacts activated inside the rollout, we characterize the corresponding discrete updates and bound the effect of moving obstacles on regular operating sets. Simulations on a 40-DOF multi-chain system show that intermediate horizons balance anticipation, responsiveness, and computational cost. Hardware experiments on a 6-DOF platform demonstrate consistent sim-to-real behavior without accurate inertial parameter estimation, and comparisons against dynamic optimization fabrics and model predictive control (MPC) baselines show improved success rates in dynamic clutter while preserving solve times compatible with real-time execution in the tested regimes.

发表机构

• Biorobotics Institute and the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna（生物机器人研究所和机器人与人工智能卓越系，圣安娜高等学校）

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