论文标题
混合系统差异动态编程，用于腿部机器人的全身运动计划
Hybrid Systems Differential Dynamic Programming for Whole-Body Motion Planning of Legged Robots
论文作者
论文摘要
本文介绍了具有基于状态开关的混合系统的轨迹优化（TO）的差异动态编程（DDP）框架。拟议的混合系统DDP（HS-DDP）方法被考虑用于使用腿部机器人进行全身运动计划。具体而言，HS-DDP结合了三种算法进步：在腿部运动中解决影响事件的影响意识到的DDP步骤，一种与开关限制的增强Lagrangian（al）方法，以及开关时间优化（STO）算法，可通过利用DDP的结构来优化开关时间。此外，一种轻松的障碍（REB）方法用于管理不平等约束，并将其集成到HS-DDP中以进行运动计划。开发算法的性能在执行边界步态的MIT MINI Cheetah的模拟模型上进行了基准测试。我们证明了AL和REB在处理开关限制，摩擦约束和扭矩限制方面的有效性。通过与以前的解决方案进行比较，我们表明STO算法的总切换时间减少了2.3倍，证明了我们方法的效率。
This paper 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.
