While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), and contact (e.g., gait) problems. To accelerate the planning process, we propose distributed optimization frameworks based on Alternating Direction Methods of Multipliers (ADMM) to solve the original large-scale Mixed-Integer NonLinear Programming (MINLP). The resulting frameworks use Mixed-Integer Quadratic Programming (MIQP) to solve contact and NonLinear Programming (NLP) to solve nonlinear dynamics, which are more computationally tractable and less sensitive to parameters. Also, we explicitly enforce patch contact constraints from limit surfaces with micro-spine grippers. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45{\deg} with a much shorter planning time.

翻译：虽然对腿形机器人的移动规划取得了巨大成功,但对具有多指抓取功能的腿形机器人的动作规划尚未成熟。 我们提出了一个高效的动作规划框架,用于同时解决移动(如环球动力学)、抓捉(如补丁接触)和接触(如步态接触)问题。 为加快规划过程,我们提议基于多尖形机器人不同方向方法(ADMMM)的分布式优化框架,以解决最初的大型混合-非激光编程(MINLP ) 。 由此产生的框架使用混合- Inter 二次编程(MIQP) 来解决接触和无线编程(NLP), 以解决非线状动态(如补丁接触) 问题, 这些问题在计算上更易行, 对参数不那么敏感。 另外, 我们明确要求用微吸附带控制器限制表面的补丁接触限制。 我们在硬件实验中展示了我们提议的框架, 显示多层机器人能够实现各种动作, 包括以更短的斜度角度45的斜度规划。