We believe that the future of robot motion planning will look very different than how it looks today: instead of complex collision avoidance trajectories with a brittle dependence on sensing and estimation of the environment, motion plans should consist of smooth, simple trajectories and be executed by robots that are not afraid of making contact. Here we present a "contact-aware" controller which continues to execute a given trajectory despite unexpected collisions while keeping the contact force stable and small. We introduce a quadratic programming (QP) formulation, which minimizes a trajectory-tracking error subject to quasistatic dynamics and contact-force constraints. Compared with the classical null-space projection technique, the inequality constraint on contact forces in the proposed QP controller allows for more gentle release when the robot comes out of contact. In the quasistatic dynamics model, control actions consist only of commanded joint positions, allowing the QP controller to run on stiffness-controlled robots which do not have a straightforward torque-control interface nor accurate dynamic models. The effectiveness of the proposed QP controller is demonstrated on a KUKA iiwa arm.
翻译:我们认为,机器人运动规划的未来将看起来与今天看起来大相径庭:与其在对环境的感知和估计上依赖极差的复杂避免碰撞轨迹相比,运动计划应当由光滑、简单的轨迹组成,由不怕接触的机器人执行。在这里,我们展示了一个“接触感知”控制器,尽管发生了意外碰撞,它仍然在继续执行特定的轨道,同时保持接触力量稳定与小的状态。我们引入了二次程序(QP)配方,它最大限度地减少轨道跟踪错误,但受准静态动态和接触力制约。与典型的无空间预测技术相比,拟议的QP控制器对接触力的不平等限制使得在机器人失去接触时能够更温和地释放。在准静态动态模型中,控制器的行动仅包括指挥的联合位置,允许QP控制器运行固态控制的机器人,而该机器人没有直径的托控制界面和精确的动态模型。拟议的QP控制器的有效性在KUKAiwa臂上展示。