A Mode-Switching Motion Control System for Reactive Interaction and Surface Following Using Industrial Robots

Abstract

This work proposes a sensor-based control system for fully automated object detection and exploration (surface following) with a redundant industrial robot. The control system utilizes both offline and online trajectory planning for reactive interaction with objects of different shapes and color using RGB-D vision and proximity/contact sensors feedback where no prior knowledge of the objects is available. The RGB-D sensor is used to collect raw 3D information of the environment. The data is then processed to segment an object of interest in the scene. In order to completely explore the object, a coverage path planning technique is proposed using a dynamic 3D occupancy grid method to generate a primary (offline) trajectory. However, RGB-D sensors are very sensitive to lighting and provide only limited accuracy on the depth measurements. Therefore, the coverage path planning is then further assisted by a real-time adaptive path planning using a fuzzy self-tuning proportional integral derivative (PID) controller. The latter allows the robot to dynamically update the 3D model by a specially designed instrumented compliant wrist and adapt to the surfaces it approaches or touches. A modeswitching scheme is also proposed to efficiently integrate and smoothly switch between the interaction modes under certain conditions. Experimental results using a CRS-F3 manipulator equipped with a custom-built compliant wrist demonstrate the feasibility and performance of the proposed method.

PID++: A Computationally Lightweight Humanoid Motion Control Algorithm

“…This paper, however, aims to tackle this problem in more detail. The proposed algorithmic approach in this paper is the simplest and most dynamically adaptive technique for PID self-tuning, providing a complete motion control solution, unlike the prior related work [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] and other algorithms [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ], for generalized humanoid motion control.…”

Whole-Body Motion Planning for a Six-Legged Robot Walking on Rugged Terrain

“…In addition, motion control is crucial for the experiment implementation. Various robot control methods have been proposed, effectively improving the performance of robots [26][27][28]. In this experiment, our aim is to validate the proposed motion planning method, as a result, each joint is just PD controlled to ensure accurate leg movements, and impedance control is added in each leg-end to deal with ground reaction force.…”

Motion Planning and Reactive Control Algorithms for Object Manipulation in Uncertain Conditions

“…Finally, this technology is not limited to a simple "plan and act" approach, but it reacts to uncertainties and to dynamic changes in the scene. In fact, the robot is endowed with a reactive control capability-both in the grasp, through sensorized fingers [2,15], and in the robot motion, through distributed proximity sensors [16]-and corresponding control algorithms that can adapt the planned task to the needs of the robot acting in the real world.…”