Book•
Robot dynamics and control
01 Jan 1989-
TL;DR: This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control, providing background material on terminology and linear transformations and examples illustrating all aspects of the theory and problems.
Abstract: From the Publisher:
This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. Provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. Includes many worked examples, examples illustrating all aspects of the theory, and problems.
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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
TL;DR: The investigation of how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented, suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.
Abstract: We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.
2,505 citations
Cites background from "Robot dynamics and control"
...Let us start by considering the arm’s dynamics in generalized coordinates (cf. Spong and Vidyasagar, 1989, p 131): we indicate by q a point in configuration space (e.g., an array ofjoint angles) and by 4 and 4 its first and second time derivatives....
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TL;DR: A number of promising applications are identified and an overview of recent developments in this domain is provided, including work on whole-body or hand motion and the various methodologies.
2,045 citations
Cites background or methods from "Robot dynamics and control"
...Relevant rotations are generally described by their three Euler angles [13] [76]....
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...One approach using such parametrized models [21] [29] [69] [70] [81] [85] [87] updates pose by inverse kinematics, a common technique in robot control theory [76]....
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Proceedings Article•
27 Nov 1995TL;DR: It is concluded that reinforcement learning can work robustly in conjunction with function approximators, and that there is little justification at present for avoiding the case of general λ.
Abstract: On large problems, reinforcement learning systems must use parameterized function approximators such as neural networks in order to generalize between similar situations and actions. In these cases there are no strong theoretical results on the accuracy of convergence, and computational results have been mixed. In particular, Boyan and Moore reported at last year's meeting a series of negative results in attempting to apply dynamic programming together with function approximation to simple control problems with continuous state spaces. In this paper, we present positive results for all the control tasks they attempted, and for one that is significantly larger. The most important differences are that we used sparse-coarse-coded function approximators (CMACs) whereas they used mostly global function approximators, and that we learned online whereas they learned offline. Boyan and Moore and others have suggested that the problems they encountered could be solved by using actual outcomes ("rollouts"), as in classical Monte Carlo methods, and as in the TD(λ) algorithm when λ = 1. However, in our experiments this always resulted in substantially poorer performance. We conclude that reinforcement learning can work robustly in conjunction with function approximators, and that there is little justification at present for avoiding the case of general λ.
1,244 citations
Cites background from "Robot dynamics and control"
...The acrobot is a two-link under-actuated robot (Figure 5) roughly analogous to a gymnast swinging on a highbar (Dejong & Spong, 1994; Spong & Vidyasagar, 1989 )....
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TL;DR: The principal contribution of the present work is to show that the control strategy can be designed in a way that greatly simplifies the application of the method of Poincare to a class of biped models, and to reduce the stability assessment problem to the calculation of a continuous map from a subinterval of R to itself.
Abstract: Biped robots form a subclass of legged or walking robots. The study of mechanical legged motion has been motivated by its potential use as a means of locomotion in rough terrain, as well as its potential benefits to prothesis development and testing. The paper concentrates on issues related to the automatic control of biped robots. More precisely, its primary goal is to contribute a means to prove asymptotically-stable walking in planar, underactuated biped robot models. Since normal walking can be viewed as a periodic solution of the robot model, the method of Poincare sections is the natural means to study asymptotic stability of a walking cycle. However, due to the complexity of the associated dynamic models, this approach has had limited success. The principal contribution of the present work is to show that the control strategy can be designed in a way that greatly simplifies the application of the method of Poincare to a class of biped models, and, in fact, to reduce the stability assessment problem to the calculation of a continuous map from a subinterval of R to itself. The mapping in question is directly computable from a simulation model. The stability analysis is based on a careful formulation of the robot model as a system with impulse effects and the extension of the method of Poincare sections to this class of models.
995 citations
Cites methods from "Robot dynamics and control"
...The dynamic model of the robot between successive impacts is easily derived using the method of Lagrange [54]....
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References
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Book•
24 Oct 1968
TL;DR: A set of heuristics is developed for moving a six degree-of-freedom manipulator from an initial position to a final position through a space containing obstacles, which results in a computer program shown to be able to direct a manipulator around obstacles.
Abstract: : The kinematics of manipulators is studied. A model is presented which allows for the systematic description of new and existing manipulators. Six degree-of-freedom manipulators are studied. Several solutions to the problem of finding the manipulator configuration leading to a specified position and orientation are presented. Numerical as well as explicit solutions are given. The problem of positioning a multilink digital arm is also discussed. Given the solution to the position problem, a set of heuristics is developed for moving a six degree-of-freedom manipulator from an initial position to a final position through a space containing obstacles. This results in a computer program shown to be able to direct a manipulator around obstacles. (Author)
672 citations
25 Mar 1985
TL;DR: Methods for avoiding singularities and improving the kinematic and static performances are presented and an optimization method is developed to find the configuration that provides the most uniform velocity ratio and an adequate mechanical advantage in all directions.
Abstract: The instantaneous kinematic and static characteristics of wrist joints are analyzed. Velocity ratio and mechanical advantage have been used for characterizing single-input-single-output mechanisms. These concepts are extended to multi-input-multi-output mechanisms in order to analyze robotic devices, specifically, wrist joints. The kinematic and static performance of wrist joints and their singularities are analyzed in terms of the generalized velocity ratio and the generalized mechanical advantage. Methods for avoiding singularities and improving the kinematic and static performances are then presented. The kinematic and static performance is optimized by varying the kinematic structure and the geometry of wrist joints. In particular the geometry of the last link, on which the end effector is mounted, is modified so that the singular points are moved from the middle of the workspace to its boundaries. The kinematic and static performance is further improved by determining the optimal configuration for performing a given task. An optimization method is developed to find the configuration that provides the most uniform velocity ratio and an adequate mechanical advantage in all directions. The kinematic and static performance of two different wrist joints are then evaluated and improved using this design methodology.
149 citations
TL;DR: In this paper, an efficient algorithm for the calculation of the inverse kinematic accelerations for a six-degree-of-freedom manipulator with a spherical wrist was presented.
Abstract: An efficient algorithm is presented for the calculation of the inverse kinematic accelerations for a six-degree-of-freedom manipulator with a spherical wrist. The inverse kinematic calculation is shown to work synergistically with the inverse dynamic calculation, producing kinematic parameters needed in the recursive Newton-Euler dynamics formulation. Additional savings in the dynamic computation are noted for a class of kinematically well-structured manipulators, such as spherical-wrist arms, and for manipulators with simply structured inertial parameters.
137 citations
"Robot dynamics and control" refers background in this paper
...SupposeU =(Uij) E SO ( 3 ) is given and Rgis the Euler angle transforma tion (2.3.9)....
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...with R E SO ( 3 ) , find (one or all) solutions of the equation...
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Book•
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01 Jan 1986
TL;DR: In this paper, basic fundamentals in robotics are presented in a tutorial, including robot arm kinematics, dynamics, planning or manipulator trajectories, servo control for manipulators, force sensing and control; robot vision systems; robot programming languages; and machine intelligence and robot planning.
Abstract: Basic fundamentals in robotics are presented in this tutorial. Topics covered are as follows: robot arm kinematics; robot arm dynamics; planning or manipulator trajectories; servo control for manipulators; force sensing and control; robot vision systems; robot programming languages; and machine intelligence and robot planning.
61 citations