Abstract
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.
Abstract (translated)
对机器人操纵器广泛部署的一个关键挑战是在任意环境中确保安全性,同时实时生成新的运动计划。特别是,必须确保操纵器不与障碍物相撞,不与自身相撞,或超过其连接扭矩限制。这一挑战加剧了因为需要考虑操纵对象的质量和惯性的不确定性,以及可能包括机器人本身的不确定性。本文提出了解决这个问题的方法,即提出了基于不确定性 reachability 的优化方法 (ARMOUR),为串行连接操纵器提供了一个可证明的安全、远离 horizon 的运动规划器和跟踪控制器框架。ARMOUR 首先构建了一个可靠的、被动式的控制器,被证明可以使操纵器在有限制误差的情况下跟踪想要的轨迹,尽管存在不确定性动态。接下来,ARMOUR 使用Recursive Newton-Euler 算法的新颖变体来计算所需的所有可能输入,以跟踪任何想要的轨迹的连续范围。最后,方法计算了操纵器的视野体积的过度近似,这使可以制定一个可以在实时中解决的问题,以合成可证明的安全运动。本文与现有方法进行了比较,并在模拟和真实硬件上展示了多种挑战性的操纵示例,例如在障碍物周围操纵一个不确定质量的哑铃。
URL
https://arxiv.org/abs/2301.13308
