CRII: RI: Practical Algorithms for Robust Feedback Motion Planning Through Contact

尽管在机器人运动规划和控制方面取得了重大进展，但现代机器人仍然难以在计划外干扰、状态不确定性和模型误差的存在下稳健地运行。事实上，最近的DARPA机器人挑战赛戏剧性地表明，世界上一些最先进的机器人仍然将与环境的接触降至最低，在现实操作场景中经常失败。这为机器人进入关键的灾难应对、探索和工业应用创造了巨大的障碍。明确考虑鲁棒性的算法需要运动规划和反馈设计的耦合，其中系统对干扰源的闭环系统响应是优化的。由于求解此类问题的计算量往往很大，它们在现代野战机器人中的应用一直受到限制。该方案的研究目标是解决具有非线性动力学以及状态和输入约束(包括摩擦接触约束)的机器人鲁棒运动规划的理论、计算和应用挑战。本研究中考虑的算法将建立在直接轨迹优化方法的基础上，同时1)支持复杂的状态约束和刚体接触，2)利用扰动和反馈控制器集中的数学结构来构造计算高效的算法。这些算法在广泛感兴趣的任务中提高稳定性的能力将被调查，例如两足行走。算法将在物理机器人的移动和操纵实验中对照最先进的方法进行评估。

Despite significant progress in robot motion planning and control, modern robots still struggle to operate robustly in the presence of unplanned disturbances, state uncertainty, and model errors. Indeed, the recent DARPA Robotics Challenge dramatically demonstrated that some of the world's most advanced robots still minimize contact with their environments and fail often in realistic operating scenarios. This creates a significant barrier to unleashing robots into critical disaster response, exploration, and industrial applications. Algorithms that explicitly reason about robustness require a coupling of motion planning and feedback design, in which the system's closed-loop response to disturbance sources is optimized. Due to the often heavy computational demands of solving such problems, their application to modern field robots has so far been limited.The research objective of this proposal is to address the theoretical, computational, and applied challenges of robust motion planning for robots with nonlinear dynamics and state and input constraints, including constraints arising from frictional contact. The algorithms under consideration in this research will build on direct trajectory optimization methods to simultaneously 1) support complex state constraints and rigid-body contacts and 2) exploit mathematical structure in disturbance and feedback controller sets to construct computationally-efficient algorithms. The ability of these algorithms to improve stability in tasks of broad interest, such as bipedal walking, will be investigated. Algorithms will be evaluated against state-of-the-art methods in locomotion and manipulation experiments on physical robots.