Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This paper proposes Autonomous Robust Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for robotic manipulators to address these challenges. ARMOUR first constructs a robust controller that tracks desired trajectories with bounded error despite uncertain dynamics. ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs required to track any trajectory within a continuum of desired trajectories. Finally, ARMOUR over-approximates the swept volume of the manipulator; this enables one to formulate an optimization problem that can be solved in real-time to synthesize provably-safe motions. This paper compares ARMOUR to state of the art methods on a set of challenging manipulation examples in simulation and demonstrates its ability to ensure safety on real hardware in the presence of model uncertainty without sacrificing performance. Project page: https://roahmlab.github.io/armour/.

翻译：确保在任意环境中实现安全、实时的运动规划，要求机器人机械臂避免碰撞、服从关节限制，并考虑物体及机器人自身质量和惯性的不确定性。本文提出基于不确定性感知可达性的自主鲁棒操作优化框架（ARMOUR），这是一种用于机器人机械臂的可证明安全的递推时域轨迹规划与跟踪控制器框架，以应对上述挑战。ARMOUR首先构建一个鲁棒控制器，该控制器能在动力学不确定的情况下以有界误差跟踪期望轨迹。随后，ARMOUR采用一种新颖的递归牛顿-欧拉方法，计算跟踪连续期望轨迹集中任意轨迹所需的所有输入。最终，ARMOUR对机械臂扫掠体积进行过近似，使得能够构建一个可实时求解的优化问题，从而综合出可证明安全的运动。本文在仿真环境中通过一系列具有挑战性的操作实例将ARMOUR与现有先进方法进行对比，并展示了其在真实硬件上面对模型不确定性时仍能确保安全且不牺牲性能的能力。项目页面：https://roahmlab.github.io/armour/。