Conventional Robot Control Research Articles

Highly flexible robotic manufacturing cell based on holistic real-time model-based control

Industrial robots in manufacturing systems offer high flexibility: In addition to their potential use in different processes such as handling and assembly, their tasks can be reconfigured in software after commissioning and even during operation. Manufacturing cells that primarily use robots or other programmable subsystems can therefore offer a high degree of flexibility, as different products can be processed in the same system as well as the system being simpler to scale up or down for changing demand. Conventional robot control (RC) systems, as found in commercial industrial robots, typically only control the tasks of single robots or a select few of robots and their accessories and tools. Offline planning and programming tools allow the different RC’s programming to be derived for a specific cell layout and overall task. This conventional approach has a number of limitations that negatively affect the system’s flexibility: the derived programming is only valid for a specific layout, also, assumptions have to be made regarding all possible collisions that arise from both static and moving objects in the cell at the time of planning, making the integration of autonomous vehicles and other systems (e.g., CNC machines) more difficult. This work proposes a control system that uses a holistic model of the manufacturing cell, comprised of all static and dynamic objects, for collision detection and path planning for directly controlling all actuators inside the cell in real-time. This system allows for interaction with non-directly controllable systems within the cell, e.g., AGVs. The requirements for this control system are derived from a reference: a robotic manufacturing cell comprised of multiple robots with overlapping kinematic work areas, multiple selected processes and their respective equipment as well as AGVs. The physical instance of this reference cell can also be used for validating and demonstrating of the controls system.

Control System of Remote Maintenance Robots for the Fusion Device ITER

Remotely operated robotic systems are vital for maintaining the ITER plant—the world largest fusion device. To operate the ITER Blanket Remote Handling System (BRHS), its control system must have functionalities that are not familiar in conventional robot control systems; e.g., completely remote operation, multi-device control with reconfiguration during operations, and large-scale operation programming. We designed the control system to solve those problems: split the control system into the High-Level Control System, which has HMI functions, and Low-Level Control System, which has interfaces with robots, and enabled the remote operation from a different building; implemented abstract commands from the High-Level to the Low-Level Control system so that the robot devices can be reconfigured during an operation; developed hierarchical operation program that manages 2-year operations. We also designed specific functions of both levels of the control system and GUI of main components of the High-Level Control System.

2P1-G04 多脚移動ロボットのための階層構造を持つ汎用制御系の研究

Control of multilegged robots requires usually complex system and many kinds of processing, because they have many DOF and are operated adaptively on complicated environment with sensors. In conventional robot control system, usually this processing had been implemented in one computer. However, considering the maintenance and scalability, it is not desirable to concentrate all processes on a huge single program. In this presentation, we introduce a general-purpose control system with hierarchical structure for multilegged robots. The control system consists of multiple computers connected via LAN. The detail of implemented system with a microcomputer and a PC and experimental results with one/four/six legged robot are shown.

Design of Autonomous Robot Soccer System Based on Sugeno Fuzzy

This paper presents the use of a fuzzy logic controller combined with Machine Vision to improve the accuracy to identify ball and speed to approaching ball in Robot Soccer. The conventional robot control consists of methods for path generation and path following. When a robot moves away the desired track, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. To achieve good control performance and requirements for timeliness, accuracy to identify and approach the ball, the paper designs a sugeno fuzzy control system, which could make robot complete the whole football performance, including finding, approaching and shooting ball. At the end of the paper, to validate the proposed fuzzy algorithm, the experiment on METALFIGHTER-2 humanoid robot has been run, which showed that the fuzzy controller is effectiveness and robustness.

Research on Humanoid Robot Soccer System Based on Fuzzy Logic

This paper presents the use of a fuzzy logic controller combined with Machine Vision to improve the accuracy to identify the ball and speed of approaching the ball in Robot Soccer. The conventional robot control consists of methods for path generation and path following. When a robot moves away the desired track, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. To achieve good control performance and requirements for timeliness, accuracy to identify and approach the ball, the paper designs a sugeno fuzzy control system, which could make robot complete the whole football performance, including finding, approaching and shooting ball. At the end of the paper, to validate the proposed fuzzy algorithm, the experiment on METALFIGHTER-2 humanoid robot has been run, which showed that the fuzzy controller is effectiveness and robustness.

Design of the Passive Dynamic Walking Robot by Applying its Dynamic Properties

Most mobile robot development has adopted model-based control. On the other hand, we focused on the passive dynamic walking robot that walks only by its dynamics. If the principle of the passive dynamic walking robot is analyzed and clarified, we could apply it to conventional walking robot control, and improve the performance of it. For this reason we tried to develop new design of the passive dynamic walking robot. In this paper we describe the robot’s dynamic properties, and propose a new design method applying these properties.

Experimental Control of a Two-Dof Flexible Robot Manipulator by Optimal and Sliding Methods

This paper presents an experimental study of a robust control scheme for flexible-link robotic manipulators. The design is based on a simple strategy for trajectory tracking which exploits the two-time scale nature of the flexible part and the rigid part of the dynamic equations of this kind of robotic arms: A slow subsystem associated with the rigid motion dynamics and a fast subsystem associated with the flexible link dynamics. Two experimental approaches are considered. In a first test an LQR optimal design strategy is used, while a second design is based on a sliding-mode scheme. Experimental results on a laboratory two-dof flexible manipulator show that this composite approach achieves good closed-loop tracking properties for both design philosophies, which compare favorably with conventional rigid robot control schemes.

反射とその適応的な抑制による腱駆動型ロボットの軌道追従制御

In this paper, a trajectory tracking control method for the tendon-driven robot is presented. In the human motor control system, it is known that the spinal reflex and the cerebellum play a key role to achieve the desired limb motions and have some characteristic structures different from the conventional robot control systems, such as the internal positive feedback loops. From the viewpoint of control, the positive feedback is inconvenient since it might potentially destabilize the limb motion. However, in the biological system, the positive feedback is actively used rather than avoided and has significant role to achieve the natural human limb motions. Thus, in this paper, we attempted to establish a trajectory tracking controller for the tendon-driven robots with reference to the biological control system composed of the spinal cord and cerebellum. The stability of the controlled system was proven along the Popov's hyper-stability theory. The effectiveness of the proposed controller was demonstrated through some computer simulations and experiments using a 2 link tendon-driven robot.

A Neuro-Fuzzy Based Fault Tolerant Control Concept for Smart Multi-Sensory Robots

Flexible and safe control of smart multi-sensory robots which have to interact with a complex environment is a tough problem due to the extremely nonlinear and variable system behavior or the conflicting goals within the different process phases. Since conventional robot control concepts can not comply with it the introduction of a hierarchical Neuro-Fuzzy (NF) based fault tolerant control concept is proposed in this paper.

Robot control system for safe and rapid programming of grinding applications

Presents a robot control system dedicated for grinding and deburring robots. The control system is based on an active force feedback system using three axes force sensor attached to the robot’s end effector. This system offers new functionality in rapid programming of the robot by applying automatic programming and force supervision. The system is implemented and tested experimentally on a MultiCraft 560 robot with parallel kinematics. The experimental results show a significant reduction in the programming and set up time compared to conventional robot control systems.

Robot Control Optimization Using Reinforcement Learning

Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.

Nonlinear adaptive control for flexible-link manipulators

As has been realized, the flexible-link manipulators have attracted more and more attention from robot control theorists and/or robot users because of its various potential advantages. But since the control degree-of-freedom is much less than that of the system, many control strategies which succeed in the conventional rigid robot control cannot be directly used in the flexible robot control problems. In this paper, a nonlinear control scheme has been proposed as a solution to these control problems. In particular, to cope with the existing model uncertainty, an adaptive version of this nonlinear control law has been proposed. Stability proof of the overall closed-loop system is then given via Lyapunov analysis. In addition, extensive experimental results are also provided to demonstrate the effectiveness of the proposed controller.

Toward the implementation of hybrid position/force control in industrial robots

With the goal of filling the gap between theory and industrial applications, an implicit hybrid control scheme is proposed in this paper, designed to fit as much as possible the conventional industrial robot control architecture. The dynamic effects due to joint compliance, which are a major source of performance degradation in industrial robots, are fully taken into account. The scheme is based on a task description particularly suited for a direct integration in conventional robot programming tools and aims at exerting the force control action without affecting the trajectory tracking. Only the differential kinematic model (Jacobian) of the robot is needed in the design of the force control law, while the force control loop is charged with rejecting dynamic disturbances due to motion. A thorough experimental validation of the strategy, both in terms of force regulation and trajectory tracking capabilities, is discussed, based on experiments performed on an industrial robot, endowed with a six-axis wrist force/torque sensor and with a laser distance sensor.

Robust control of free-floating space robot systems

In this paper, we propose a robust control scheme for a free-floating space robot system where a robot arm is mounted on a free-floating base—a satellite, spacecraft or space station. The base is not controlled by external moments or forces. We model the system as an extended robot, including a pseudo-arm representing the base motion produced by six hypothetical passive joints, and a real robot arm. This model allows us to categorize the free-floating space robot system as a specific type of under-actuated system with mixed passive and active joints. We then discuss some fundamental properties of such a system. By means of an input-output linearization technique, we demonstrate that the internal dynamics of the system are nonlinear parametric and therefore, the control of the system using conventional robot control schemes is not feasible. To overcome the difficulty in controlling the internal dynamics subject to parameter uncertainty, and to avoid the measurement of the base acceleration, we develop a ro...

A Fuzzy Logic-Based Robot Controller

Conventional robot controllers rely on an exact inverse kinematic model to convert user-specified Cartesian trajectory commands to joint set points. These inverse kinematic equations are robot specific and form a set of highly coupled nonlinear equations that pose a considerable computational bottleneck. It is not uncommon for the host CPU to dedicate as much as 80% of its computational resources to solve the inverse kinematic equations alone. Further, the strictly mathematical nature of the inverse kinematic equations results in kinematic singularities in which the robot is uncontrollable. These drawbacks have resulted in current robot controllers being limited in flexibility with respect to adaptive control, kinematic redundancy, and obstacle avoidance. This observation provided the impetus to investigate the applicability of fuzzy logic to resolve the computational bottleneck of the inverse kinematic module. In the following, an approach is presented based on fuzzy logic, which replaces the computationally intensive inverse kinematic equations and lays the foundation for a robot controller with a simpler architecture. A hardware implementation of a fuzzy logicbased robot controller is described in which simple fuzzy rules replace the complex inverse kinematic equations. The implemented fuzzy robot controller was tested on a planar four-degree-of-freedom lab robot. Hardware test results of the lab robot tracking arbitrary trajectories are discussed.

Iterative Learning Control of Flexible-Joint Robots using Neural Networks

Abstract Due to the increased complexity of the dynamics of robots with joint flexibility, many conventional robot control strategies are incapable to solve this problem effectively. To overcome this difficulty, a neural controller based on iterative learning has been proposed for the control of a flexible joint robot in this paper. The controller design is based the fact that most industrial robots perform repetitive tasks. The neural network for this purpose is trained as an inverse dynamic model of a flexible joint robot and is implemented as a feedforward controller. Our modifications of the back-propagation neural network [11] are employed in this study and result in a much better learning efficiency. A case study of a two-link flexible joint robot demonstrated that the proposed iterative neural learning controller can reduce the tracking errors after a few learning steps. Also, the proposed neural learning has advantages of easy implementation and requiring very little on-line computation.

A model-based manipulation system with skill-based execution

A model-based robot system is described. This system was developed to replace and improve upon the conventional robot control scheme of teaching-by-showing and replay. The key concept of this system consists of the combination of manipulation skills with an environment model. The model provides the geometric structure and the physical properties of the objects in the environment. Manipulation skills enable reliable task execution in the presence of unavoidable errors and uncertainties. Skills are implemented within the hybrid position/velocity/force control scheme and incorporate sensing procedures to detect the state to be achieved. An experiment involving the disassembly of a valve using this system is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

環境モデルと作業スキルの統合によるロボット作業システム

This paper describes a model-based robot system. This system was developed to replace and improve upon the conventional robot control scheme of teaching-by-showing and replay. The key concept of this system consists of the combination of manipulation skills with an environment model. The model provides the geometric structure and physical properties of the objects in the environment. An interactive geometric modeling system has been developed to generate accurate model through a good manmachine interface. The manipulation skills enable reliable task execution in the presence of unavoidable errors and uncertainties. Using the ETA-3 direct-drive manipulator, skills are implemented within the hybrid (position/velocity/torque) control scheme and are incorporated with sensing procedures to detect the state to be achieved, disassembly of a valve has been successfuly achieved by using this system.

Model-Based On-Site Description of Robotized Processings

The conventional robot control methods are too laborious and time-consuming to allow an economic use of robots in small series production. To overcome the problem a model-based on-site description method of processings like drilling, arc welding and grinding is presented. The availability of the models of the tool and the workpieces restricts the need for on-site information of each different processing to the values of a few parameters and the necessary 3D-points and dimensions to separate the impact area of the tool. With the presented method the robotized execution of an individual job takes less of the time the human worker spends when doing the same job manually. By using an advanced 3D-measuring device the operator can supervise and control several robots simultaneously. Man-machine interaction requirements are considered.