A key challenge to the widespread deployment of robotic manipulators is the need to ensure safety in arbitrary environments while generating new motion plans in real-time. In particular, one must ensure that a manipulator does not collide with obstacles, collide with itself, or exceed its joint torque limits. This challenge is compounded by the need to account for uncertainty in the mass and inertia of manipulated objects, and potentially the robot itself. The present work addresses this challenge by proposing Autonomous Robust Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for serial link manipulators. ARMOUR works by first constructing a robust, passivity-based controller that is proven to enable a manipulator to track desired trajectories with bounded error despite uncertain dynamics. Next, ARMOUR uses a novel variation on the Recursive Newton-Euler Algorithm (RNEA) to compute the set of all possible inputs required to track any trajectory within a continuum of desired trajectories. Finally, the method computes an over-approximation to the swept volume of the manipulator; this enables one to formulate an optimization problem, which can be solved in real-time, to synthesize provably-safe motion. The proposed method is compared to state of the art methods and demonstrated on a variety of challenging manipulation examples in simulation and on real hardware, such as maneuvering a dumbbell with uncertain mass around obstacles.

翻译：机器人机械臂广泛部署的关键挑战之一，是在实时生成新运动规划的同时确保任意环境中的安全性。具体而言，必须确保机械臂不与障碍物碰撞、不发生自碰撞，且不超过关节力矩限制。这一挑战因需考虑操作对象（以及潜在的自身体）的质量和惯量不确定性而进一步加剧。本文提出一种名为"基于不确定性感知可达性的自主鲁棒操作优化"（ARMOUR）的框架，通过构建可证明安全的滚动时域轨迹规划器与跟踪控制器，专门针对串联机械臂解决上述问题。ARMOUR首先构建一个鲁棒的无源控制器，该控制器被证明能在动力学不确定情况下使机械臂以有界误差跟踪期望轨迹。其次，ARMOUR采用递归牛顿-欧拉算法（RNEA）的新型变体，计算在连续期望轨迹域内跟踪任意轨迹所需的所有可能输入集合。最后，该方法通过计算机械臂扫掠体积的超近似，构建可实时求解的优化问题以合成可证明安全的运动。本文所提方法与现有技术进行对比，并在仿真及真实硬件上开展一系列具有挑战性的操作实例验证，例如操控质量不确定的哑铃绕行障碍物等场景。