发表机构
Sungkyunkwan University (SKKU); AIDIN ROBOTICS Inc.（成均馆大学; 艾丁机器人公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对经典DDP处理控制约束的局限，提出ABC-DDP算法，结合可行性驱动多射击框架，实现欠驱动腿式机器人实时MPC，仿真验证其可完成静态双腿站立及多种动态运动。
AI 中文摘要
本文提出了一种用于欠驱动腿式机器人的实时控制约束差分动态规划（DDP）框架。针对经典DDP处理控制约束的局限性，我们提出了一种基于加速投影梯度（APG）的控制约束DDP（ABC-DDP），该算法可高效计算约束解并识别有效集，无需重复的Karush-Kuhn-Tucker（KKT）逆运算。引入虚拟约束以在可行性驱动的多射击框架内整合控制约束，即使从动态不可行的初始条件也能实现稳定优化。该方法支持在强欠驱动下使用短预测时域的实时模型预测控制（MPC）。仿真结果表明，在统一MPC框架内，可实现受外部干扰下的静态双腿站立，以及慢步态、直立行走、高速奔跑等多种动态运动。据我们所知，这是首次通过实时有限时域MPC实现四足机器人的静态双腿站立。
英文摘要
This paper presents a real-time control-constrained Differential Dynamic Programming (DDP) framework for underactuated legged robots. To address the limitation of classical DDP in handling control constraints, we propose an Accelerated Projected Gradient (APG)-based control-constrained DDP (ABC-DDP), which efficiently computes constrained solutions and identifies active sets without repeated Karush-Kuhn-Tucker (KKT) inversions. A virtual constraint is introduced to integrate control constraints within a feasibility-driven multiple-shooting framework, enabling stable optimization even from dynamically infeasible initializations. The proposed method supports real-time model predictive control (MPC) with short horizons under strong underactuation. Simulation results demonstrate static two-leg standing under external disturbances, along with diverse dynamic motions including slow catwalk, upright walking, and high-speed running within a unified MPC framework. To the best of our knowledge, this is the first demonstration of static two-leg standing of a quadruped robot achieved using real-time finite-horizon MPC.
CommentsThis version includes a minor correction to the notation in Eq. (2)
Journal refIEEE Robotics and Automation Letters (RA-L), 2026
