Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Modeling and Control of the Cooperative Automated Fiber Placement System

The Automated Fiber Placement (AFP) machines have brought significant improvement on composite manufacturing. However, the current AFP machines are designed for the manufacture of simple structures like shallow shells or tubes, and not capable of handling some applications with more complex shapes. A cooperative AFP system is proposed to manufacture more complex composite components which pose high demand for trajectory planning than those by the current APF system. The system consists of a 6 degree-of-freedom (DOF) serial robot holding the fiber placement head, a 6-DOF revolute-spherical-spherical (RSS) parallel robot on which a 1-DOF mandrel holder is installed and an eye-to-hand photogrammetry sensor, i.e. C-track, to detect the poses of both end-effectors of parallel robot and serial robot. Kinematic models of the parallel robot and the serial robot are built. The analysis of constraints and singularities is conducted for the cooperative AFP system. The definitions of the tool frames for the serial robot and the parallel robot are illustrated. Some kinematic parameters of the parallel robot are calibrated using the photogrammetry sensor. Although, the cooperative AFP system increases the flexibility of composite manufacturing by adding more DOF, there might not be a feasible path for laying up the fiber in some cases due to the requirement of free from collisions and singularities. To meet the challenge, an innovative semi-offline trajectory synchronized algorithm is proposed to incorporate the on-line robot control in following the paths generated off-line especially when the generated paths are infeasible for the current multiple robots to realize. By adding correction to the path of the robots at the points where the collision and singularity occur, the fiber can be laid up continuously without interruption. The correction is calculated based on the pose tracking data of the parallel robot detected by the photogrammetry sensor on-line. Due to the flexibility of the 6-DOF parallel robot, the optimized offsets with varying movements are generated based on the different singularities and constraints. Experimental results demonstrate the successful avoidance of singularities and joint limits, and the designed cooperative AFP system can fulfill the movement needed for manufacturing a composite structure with Y-shape

Visual Calibration, Identification and Control of 6-RSS Parallel Robots

Parallel robots present some outstanding advantages in high force-to-weight ratio, better stiffness and theoretical higher accuracy compared with serial manipulators. Hence parallel robots have been utilized increasingly in various applications. However, due to the manufacturing tolerances and defections in the robot structure, the positioning accuracy of parallel robots is basically equivalent with that of serial manipulators according to previous researches on the accuracy analysis of the Stewart Platform [1], which is difficult to meet the precision requirement of many potential applications. In addition, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. Visual sensor is a good choice for providing non-contact measurement of the end-effector pose (position and orientation) with simplicity in operation and low cost compared to other measurement methods such as the coordinate measurement machine (CMM) [2] and the laser tracker [3]. In this research, a series of solutions including kinematic calibration, dynamic identification and visual servoing are proposed to improve the positioning and tracking performance of the parallel robot based on the visual sensor. The main contributions of this research include three parts. In the first part, a relative pose-based algorithm (RPBA) is proposed to solve the kinematic calibration problem of a six-revolute-spherical-spherical (6-RSS) parallel robot by using the optical CMM sensor. Based on the relative poses between the candidate and the initial configurations, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. The experimental results demonstrate that the proposal RPBA using optical CMM is an implementable and effective method for the parallel robot calibration. The second part focuses on the dynamic model identification of the 6-RSS parallel robots. A visual closed-loop output-error identification method based on an optical CMM sensor is proposed for the purpose of the advanced model-based visual servoing control design of parallel robots. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, the visual closed-loop output-error identification method is developed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests. In the last part, a dynamic sliding mode control (DSMC) scheme combined with the visual servoing method is proposed to improve the tracking performance of the 6-RSS parallel robot based on the optical CMM sensor. By employing a position-to-torque converter, the torque command generated by DSMC can be applied to the position controlled industrial robot. The stability of the proposed DSMC has been proved by using Lyapunov theorem. The real-time experiment tests on a 6-RSS parallel robot demonstrate that the developed DSMC scheme is robust to the modeling errors and uncertainties. Compared with the classical kinematic level controllers, the proposed DSMC exhibits the superiority in terms of tracking performance and robustness

REAL TIME POSE CONTROL OF PARALLEL ROBOT

Parallel robot is a kind of robot that uses several computer-controlled serial chains to support a single platform, or end-effector (EE). It has been widely used in the various applications such as aircraft simulator, high precision machining and aerospace manufacturing, etc. However, achieving high precision pose control of 6-DOF parallel robot makes a challenge for robotic researchers due to the lack of dynamic modeling of robot and accurate measurement of pose. The present research study aims at controlling the pose of end-effector (EE) of 6-RSS parallel platform in real time. In this thesis, the kinematics of the robot including the inverse and forward kinematics are presented. The numerical solution of forward kinematics model is provided and the forward kinematic model is implemented in the Simulink to serve as the parallel robot for the pose control design. A Simulink model is built in order to simulate and implement the pose controller for the parallel robot. The parallel robot consists of six Brush-Less DC (BLDC) actuators. Both linear and nonlinear dynamic models of the DC motors are derived. The parameters of linear dynamic models are identified using Genetic Algorithm (GA). Also, the parameters of nonlinear actuators’ dynamic model are identified using the multi-objective optimization method. Then a proportional-integralderivative (PID) controller is used to control the EE and track the desired trajectory. The simulation results demonstrate an outstanding tracking performance for the designed PID controller. To further validate the designed pose controller in the real time experiment, we use the photogrammetry sensor–C-track from Creaform Inc. to obtain the pose of EE. By comparing the desired pose with the current measured pose by C-track, the inverse kinematic model of the parallel robot is validated and the experimental results demonstrate that the inverse kinematic model is accurate enough for the subsequent real time pose control. In order to control the pose of the parallel robot, the pose measured by C-track is used to implement the real time pose feedback control system. Since the C-track is connected to one computer and the parallel robot is connected to a different computer, it is necessary to transfer the obtained pose data by C-track from one PC to the other one that controls the actuators of the parallel robot. A serial port has been used for the real time data transferring. A lot of effort has been dedicated to the retrial of the pose data in the real time pose control Simulink blocks. Finally, six PID controllers are applied to track the pose of the EE. The experimental results show an acceptable pose tracking control of the system

A New Classification and Aerial Manipulation Q-PRR Design

International audienceThis paper presents a new designation and classification of system with UAV and robot manipulator where a new nomenclature is recognized as being the first contribution in the bibliography of design and systems. Several papers deal a problem of manipulation with a different unmanned aerial vehicle, robot arms and also with different naming of their systems, where the difficulty for locate and finding items and a good paper with its title or even by keywords, multirotor equipped with n-DoF robotic arm is the expression among the most widely used to describe that system. Aerial manipulation formula is presented and proved with a large example in the literature

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Behavior policy learning: Learning multi-stage tasks via solution sketches and model-based controllers

Multi-stage tasks are a challenge for reinforcement learning methods, and require either specific task knowledge (e.g., task segmentation) or big amount of interaction times to be learned. In this paper, we propose Behavior Policy Learning (BPL) that effectively combines 1) only few solution sketches, that is demonstrations without the actions, but only the states, 2) model-based controllers, and 3) simulations to effectively solve multi-stage tasks without strong knowledge about the underlying task. Our main intuition is that solution sketches alone can provide strong data for learning a high-level trajectory by imitation, and model-based controllers can be used to follow this trajectory (we call it behavior) effectively. Finally, we utilize robotic simulations to further improve the policy and make it robust in a Sim2Real style. We evaluate our method in simulation with a robotic manipulator that has to perform two tasks with variations: 1) grasp a box and place it in a basket, and 2) re-place a book on a different level within a bookcase. We also validate the Sim2Real capabilities of our method by performing real-world experiments and realistic simulated experiments where the objects are tracked through an RGB-D camera for the first task