AlAttar Ahmad, Chappell Digby, Kormushev Petar
Robot Intelligence Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Robotics Lab , Dubai Future Labs, Dubai, United Arab Emirates.
Front Robot AI. 2022 Feb 2;9:809114. doi: 10.3389/frobt.2022.809114. eCollection 2022.
Model predictive control is a widely used optimal control method for robot path planning and obstacle avoidance. This control method, however, requires a system model to optimize control over a finite time horizon and possible trajectories. Certain types of robots, such as soft robots, continuum robots, and transforming robots, can be challenging to model, especially in unstructured or unknown environments. Kinematic-model-free control can overcome these challenges by learning local linear models online. This paper presents a novel perception-based robot motion controller, the kinematic-model-free predictive controller, that is capable of controlling robot manipulators without any prior knowledge of the robot's kinematic structure and dynamic parameters and is able to perform end-effector obstacle avoidance. Simulations and physical experiments were conducted to demonstrate the ability and adaptability of the controller to perform simultaneous target reaching and obstacle avoidance.
模型预测控制是一种广泛应用于机器人路径规划和避障的最优控制方法。然而，这种控制方法需要一个系统模型来在有限的时间范围内和可能的轨迹上优化控制。某些类型的机器人，如软体机器人、连续体机器人和变形机器人，建模可能具有挑战性，尤其是在非结构化或未知环境中。无运动学模型控制可以通过在线学习局部线性模型来克服这些挑战。本文提出了一种新颖的基于感知的机器人运动控制器——无运动学模型预测控制器，它能够在无需任何机器人运动学结构和动力学参数先验知识的情况下控制机器人操纵器，并能够执行末端执行器避障。进行了仿真和物理实验，以证明该控制器执行同时到达目标和避障的能力和适应性。
