Trajectory optimization problems for legged robots are commonly formulated with fixed contact schedules. These multi-phase Hybrid Trajectory Optimization (HTO) methods result in locally optimal trajectories, but the result depends heavily upon the predefined contact mode sequence. Contact-Implicit Optimization (CIO) offers a potential solution to this issue by allowing the contact mode to be determined throughout the trajectory by the optimization solver. However, CIO suffers from long solve times and convergence issues. This work combines the benefits of these two methods into one algorithm: Staged Contact Optimization (SCO). SCO tightens constraints on contact in stages, eventually fixing them to allow robust and fast convergence to a feasible solution. Results on a planar biped and spatial quadruped demonstrate speed and optimality improvements over CIO and HTO. These properties make SCO well suited for offline trajectory generation or as an effective tool for exploring the dynamic capabilities of a robot.
翻译:对于机器人步态优化问题,通常采用固定的接触方案设计多阶段混合轨迹优化方法,结果是得到局部优化轨迹,但结果严重依赖于预定义的接触模式顺序。接触隐式优化(CIO)提供了一个潜在的解决方案,允许通过优化求解器在整个轨迹中确定接触模式,但CIO存在长时间求解和收敛问题。本文将这两种方法的优点结合起来,提出一种名为舞台优化(SCO)的算法。SCO通过舞台式的方式收紧接触约束,最终固定它们以允许快速、稳健地收敛到可行解。在平面双足机器人和空间四足机器人上的结果表明,SCO相对于CIO和HTO具有速度和优化性能的提升。这些特性使得SCO非常适合于离线轨迹生成或作为探索机器人动态能力的有效工具。