Hybrid Systems Differential Dynamic Programming for Whole-Body Motion Planning of Legged Robots

Hybrid Systems Differential Dynamic Programming for Whole-Body Motion Planning of Legged Robots
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用于腿式机器人全身运动规划的混合系统微分动态规划

DOI:
10.1109/lra.2020.3007475
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发表时间:
2020
影响因子:
5.2
通讯作者:
Wensing, Patrick M.
Wensing, Patrick M.
中科院分区:
计算机科学2区
文献类型:
--
作者:
Li, He;Wensing, Patrick M.

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这封信提出了一个微分动态规划(DDP)框架,用于具有基于状态切换的混合系统的轨迹优化(TO)。所提出的混合系统 DDP(HS-DDP)方法被考虑应用于腿式机器人的全身运动规划。具体来说,HS-DDP 融合了三种算法进步:解决腿部运动中的碰撞事件的碰撞感知 DDP 步骤、处理切换约束的增强拉格朗日 (AL) 方法以及利用 DDP 结构优化切换时间的切换时间优化 (STO) 算法。此外,松弛障碍 (ReB) 方法用于管理不平等约束,并集成到 HS-DDP 中以进行运动规划。所开发算法的性能以麻省理工学院迷你猎豹执行跳跃步态的模拟模型为基准。我们证明了 AL 和 ReB 在处理切换约束、摩擦约束和扭矩限制方面的有效性。通过与之前的解决方案进行比较,我们发现 STO 算法将总切换时间减少了 2.3 倍,证明了我们方法的效率。
This letter presents a Differential Dynamic Programming (DDP) framework for trajectory optimization (TO) of hybrid systems with state-based switching. The proposed Hybrid Systems DDP (HS-DDP) approach is considered for application to whole-body motion planning with legged robots. Specifically, HS-DDP incorporates three algorithmic advances: an impact-aware DDP step addressing the impact event in legged locomotion, an Augmented Lagrangian (AL) method dealing with the switching constraint, and a Switching Time Optimization (STO) algorithm that optimizes switching times by leveraging the structure of DDP. Further, a Relaxed Barrier (ReB) method is used to manage inequality constraints and is integrated into HS-DDP for locomotion planning. The performance of the developed algorithms is benchmarked on a simulation model of the MIT Mini Cheetah executing a bounding gait. We demonstrate the effectiveness of AL and ReB for handling switching constraints, friction constraints, and torque limits. By comparing to previous solutions, we show that the STO algorithm achieves 2.3 times more reduction of total switching times, demonstrating the efficiency of our method.
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发表时间: 1993
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