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Journal ArticleDOI

Modelling and Control of Robot Manipulators

01 Oct 2000-Industrial Robot-an International Journal (Emerald Group Publishing Limited)-Vol. 27, Iss: 5
TL;DR: In this article, the authors tried to read modelling and control of robot manipulators as one of the reading material to finish quickly, and they found that reading book can be a great choice when having no friends and activities.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.
Citations
More filters
MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations


Cites background or methods from "Modelling and Control of Robot Mani..."

  • ...The standard approach in many robotics books [368, 858, 908, 994] is to introduce the kinematic chain formulations and DH parameters in the first couple of chapters, and then move on to topics that are crucial for controlling robot manipulators, including dynamics modeling, singularities, manipulability, and control....

    [...]

  • ...This may seem like an inverse control problem [858] or a BVP as shown in Figure 14....

    [...]

  • ...The fourth module is a well-studied control problem that is covered in numerous texts [526, 848, 858]....

    [...]

  • ...An extension of this model to motors that have gear ratios and nonnegligible mass appears in [858]....

    [...]

  • ...For further reading on robot dynamics, see [30, 206, 728, 858, 908, 994]....

    [...]

Proceedings ArticleDOI
01 Oct 2006
TL;DR: An overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field is given and how the most recently developed methods can significantly improve learning performance is shown.
Abstract: The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm

598 citations

Proceedings ArticleDOI
19 May 2008
TL;DR: The concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots is presented and the results are presented which demonstrate the effectiveness of this approach.
Abstract: Joint position control is a dominant paradigm in industrial robots. While it has been successful in various industrial tasks, joint position control is severely limited in performing advanced robotic tasks, especially in unstructured dynamic environments. This paper presents the concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots. Robot torque control is essentially accomplished by converting desired joint torques into instantaneous increments of joint position inputs. For each joint, the transformer is based on the knowledge of the joint position servo controller and the closed-loop frequency response of that joint. This transformer can be implemented as a software unit and applied to any conventional position-controlled robot so that torque command to the robot becomes available. This approach has been experimentally implemented on the Honda ASIMO robot arm. The paper presents the results of this implementation which demonstrate the effectiveness of this approach.

529 citations


Cites background from "Modelling and Control of Robot Mani..."

  • ...Since this position controller cannot account for the dynamics of the system, the dynamic coupling effects are treated as a disturbance....

    [...]

Journal ArticleDOI
TL;DR: It appears that the protocol in combination with the MT9B is valid for, and theMT9B in combinationWith the protocol is accurate when, measuring shoulder and elbow kinematics, during the tasks tested, in ambulatory settings.
Abstract: Inertial and magnetic measurement systems (IMMSs) are a new generation of motion analysis systems which may diffuse the measurement of upper-limb kinematics to ambulatory settings. Based on the MT9B IMMS (Xsens Technologies, NL), we therefore developed a protocol that measures the scapulothoracic, humerothoracic and elbow 3D kinematics. To preliminarily evaluate the protocol, a 23-year-old subject performed six tasks involving shoulder and elbow single-joint-angle movements. Criteria for protocol validity were limited cross-talk with the other joint-angles during each task; scapulohumeral-rhythm close to literature results; and constant carrying-angle. To assess the accuracy of the MT9B when measuring the upper-limb kinematics through the protocol, we compared the MT9B estimations during the six tasks, plus other four, with the estimations of an optoelectronic system (the gold standard), in terms of RMS error, correlation coefficient (r), and the amplitude ratio (m). Results indicate that the criteria for protocol validity were met for all tasks. For the joint angles mainly involved in each movement, the MT9B estimations presented RMS errors 0.99 and 0.9 < m < 1.09. It appears therefore that (1) the protocol in combination with the MT9B is valid for, and (2) the MT9B in combination with the protocol is accurate when, measuring shoulder and elbow kinematics, during the tasks tested, in ambulatory settings.

285 citations


Cites methods from "Modelling and Control of Robot Mani..."

  • ...For the distal humerus, the SoR was defined by applying the Denavit– Hartenberg method [24] at the elbow FL-EX hinge joint (Fig....

    [...]

Journal ArticleDOI
TL;DR: The results show that the power of variable impedance control is made available to a wide variety of robotic systems and practical applications and can be used not only for planning but also to derive variable gain feedback controllers in realistic scenarios.
Abstract: One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in everyday human environments. It is, however, not trivial to derive variable impedance controllers for practical high degree-of-freedom (DOF) robotic tasks. In this contribution, we accomplish such variable impedance control with the reinforcement learning (RL) algorithm PI2 (Policy Improvement with Path Integrals). PI2 is a model-free, sampling-based learning method derived from first principles of stochastic optimal control. The PI 2 algorithm requires no tuning of algorithmic parameters besides the exploration noise. The designer can thus fully focus on the cost function design to specify the task. From the viewpoint of robotics, a particular useful property of PI2 is that it can scale to problems of many DOFs, so that reinforcement learning on real robotic systems becomes feasible. We sketch the PI2 algorithm and its theoretical properties, and how it is applied to gain scheduling for variable impedance control. We evaluate our approach by presenting results on several simulated and real robots. We consider tasks involving accurate tracking through via points, and manipulation tasks requiring physical contact with the environment. In these tasks, the optimal strategy requires both tuning of a reference trajectory and the impedance of the end-effector. The results show that we can use path integral based reinforcement learning not only for planning but also to derive variable gain feedback controllers in realistic scenarios. Thus, the power of variable impedance control is made available to a wide variety of robotic systems and practical applications.

280 citations


Cites background or methods from "Modelling and Control of Robot Mani..."

  • ...…by determining the configuration at a given point in the task, the apparent inertia at the end-effector is co-determined (Hogan 1985b; Khatib 1995; Sciavicco and Siciliano 2000b), and second by possibly purposefully using a reference trajectory which is far from the actually followed trajectory…...

    [...]

  • ...The feedforward control term may come, for instance, from an inverse dynamics control component, or a computed torque control component (Sciavicco and Siciliano 2000a)....

    [...]

  • ...…impedance is computed from the desired task space impedance KP,x, KD,x by help of the Jacobian J of the forward kinematics of the robot as follows (Sciavicco and Siciliano 2000a): KP,q = JTKP,x J and KD,q = JTKD,x J. (2) Here we assume that the geometric stiffness due to the change of the…...

    [...]

  • ...forward control term may come, for instance, from an inverse dynamics control component, or a computed torque control component [24]....

    [...]

  • ...Joint space impedance is computed from the desired task space impedance KP,x,KD,x by help of the Jacobian J of the forward kinematics of the robot as follows [24]:...

    [...]

References
More filters
MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: An overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field is given and how the most recently developed methods can significantly improve learning performance is shown.
Abstract: The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm

598 citations

Proceedings ArticleDOI
19 May 2008
TL;DR: The concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots is presented and the results are presented which demonstrate the effectiveness of this approach.
Abstract: Joint position control is a dominant paradigm in industrial robots. While it has been successful in various industrial tasks, joint position control is severely limited in performing advanced robotic tasks, especially in unstructured dynamic environments. This paper presents the concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots. Robot torque control is essentially accomplished by converting desired joint torques into instantaneous increments of joint position inputs. For each joint, the transformer is based on the knowledge of the joint position servo controller and the closed-loop frequency response of that joint. This transformer can be implemented as a software unit and applied to any conventional position-controlled robot so that torque command to the robot becomes available. This approach has been experimentally implemented on the Honda ASIMO robot arm. The paper presents the results of this implementation which demonstrate the effectiveness of this approach.

529 citations

Journal ArticleDOI
TL;DR: It appears that the protocol in combination with the MT9B is valid for, and theMT9B in combinationWith the protocol is accurate when, measuring shoulder and elbow kinematics, during the tasks tested, in ambulatory settings.
Abstract: Inertial and magnetic measurement systems (IMMSs) are a new generation of motion analysis systems which may diffuse the measurement of upper-limb kinematics to ambulatory settings. Based on the MT9B IMMS (Xsens Technologies, NL), we therefore developed a protocol that measures the scapulothoracic, humerothoracic and elbow 3D kinematics. To preliminarily evaluate the protocol, a 23-year-old subject performed six tasks involving shoulder and elbow single-joint-angle movements. Criteria for protocol validity were limited cross-talk with the other joint-angles during each task; scapulohumeral-rhythm close to literature results; and constant carrying-angle. To assess the accuracy of the MT9B when measuring the upper-limb kinematics through the protocol, we compared the MT9B estimations during the six tasks, plus other four, with the estimations of an optoelectronic system (the gold standard), in terms of RMS error, correlation coefficient (r), and the amplitude ratio (m). Results indicate that the criteria for protocol validity were met for all tasks. For the joint angles mainly involved in each movement, the MT9B estimations presented RMS errors 0.99 and 0.9 < m < 1.09. It appears therefore that (1) the protocol in combination with the MT9B is valid for, and (2) the MT9B in combination with the protocol is accurate when, measuring shoulder and elbow kinematics, during the tasks tested, in ambulatory settings.

285 citations

Journal ArticleDOI
TL;DR: The results show that the power of variable impedance control is made available to a wide variety of robotic systems and practical applications and can be used not only for planning but also to derive variable gain feedback controllers in realistic scenarios.
Abstract: One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in everyday human environments. It is, however, not trivial to derive variable impedance controllers for practical high degree-of-freedom (DOF) robotic tasks. In this contribution, we accomplish such variable impedance control with the reinforcement learning (RL) algorithm PI2 (Policy Improvement with Path Integrals). PI2 is a model-free, sampling-based learning method derived from first principles of stochastic optimal control. The PI 2 algorithm requires no tuning of algorithmic parameters besides the exploration noise. The designer can thus fully focus on the cost function design to specify the task. From the viewpoint of robotics, a particular useful property of PI2 is that it can scale to problems of many DOFs, so that reinforcement learning on real robotic systems becomes feasible. We sketch the PI2 algorithm and its theoretical properties, and how it is applied to gain scheduling for variable impedance control. We evaluate our approach by presenting results on several simulated and real robots. We consider tasks involving accurate tracking through via points, and manipulation tasks requiring physical contact with the environment. In these tasks, the optimal strategy requires both tuning of a reference trajectory and the impedance of the end-effector. The results show that we can use path integral based reinforcement learning not only for planning but also to derive variable gain feedback controllers in realistic scenarios. Thus, the power of variable impedance control is made available to a wide variety of robotic systems and practical applications.

280 citations