论文标题
通过分布式优化同时进行富含接触的抓握和运动，为多限制机器人提供自由攀爬
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
论文作者
论文摘要
虽然腿部机器人的运动计划表现出了巨大的成功，但具有灵活的多指抓握的腿部机器人的运动计划还不成熟。我们提出了一个有效的运动计划框架，用于同时解决运动（例如，质心动力学），抓地力（例如，贴片接触）和触点（例如步态）问题。为了加速计划过程，我们建议基于乘数的交替方向方法（ADMM）提出分布式优化框架，以求解原始的大型混合构成非整数非线性编程（MINLP）。最终的框架使用混合构成二次编程（MIQP）求解触点和非线性编程（NLP）来求解非线性动力学，这些动力学在计算方面更可触及，对参数较不敏感。此外，我们通过微蜘蛛抓手从极限表面明确执行贴片接触约束。我们在硬件实验中演示了我们提出的框架，表明多限制机器人能够实现各种动作，包括在斜坡上以45°的斜坡自由攀爬，并且计划时间较短。
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° with a much shorter planning time.
