Abstract
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.
Highlights
Researchers over the past few decades studied optimal control, especially for applications where systems dynamics and constraints require proper handling (Zanon et al, 2017)
Model-free predictive control methods were previously studied (Stenman, 1999; Yamamoto, 2014; Li and Yamamoto, 2016), online kinematic-model-free predictive control for robot manipulators is an unexplored area of research up to our knowledge
A novel KMF controller is proposed that is capable of obstacle avoidance, using an Model Predictive Control (MPC) approach, without any prior knowledge of the robot’s model
Summary
Researchers over the past few decades studied optimal control, especially for applications where systems dynamics and constraints require proper handling (Zanon et al, 2017). The kinematic-model-free (KMF) controller is capable of controlling robot manipulators without any previous knowledge of the robot’s kinematic structure or dynamic properties. The controller works by building local linear models that are used to perform reaching tasks (AlAttar and Kormushev, 2020). The KMF controller up to date have not been applied to high-level tasks such as trajectory planning and obstacle avoidance as they are concerned with low-level control. A novel kinematic-model-free predictive controller (KMFPC), that is capable of performing end-effector reaching and obstacle avoidance for robot manipulators, is presented.
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