Industrial manipulators are normally operated in cluttered environments, making safe motion planning important. Furthermore, the presence of model-uncertainties make safe motion planning more difficult. Therefore, in practice the speed is limited in order to reduce the effect of disturbances. There is a need for control methods that can guarantee safe motions that can be executed fast. We address this need by suggesting a novel model predictive control (MPC) solution for manipulators, where our two main components are a robust tube MPC and a corridor planning algorithm to obtain collision-free motion. Our solution results in a convex MPC, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a 6 DOF industrial robot operating in cluttered environments with uncertainties in model parameters. We outperform benchmark methods, both in terms of being able to work under higher levels of model uncertainties, while also yielding faster motion.

翻译：工业机械臂通常在杂乱环境中运行，因此安全运动规划至关重要。此外，模型不确定性的存在使得安全运动规划更加困难。实践中常通过限制运行速度来降低扰动影响，亟需能够保证安全且可快速执行运动的控制方法。为应对这一需求，我们提出一种新型机械臂模型预测控制（MPC）方案，其核心由鲁棒管式MPC与用于生成无碰撞运动的走廊规划算法构成。该方案最终形成凸优化形式的MPC问题，可实现快速求解，具备实际应用价值。我们在仿真环境中验证了该方法的效果：六自由度工业机器人在存在模型参数不确定性的杂乱场景中运行。实验表明，本方法在更高模型不确定性水平下仍能稳定工作，同时生成更快的运动轨迹，综合性能优于基准方法。