一种四自由度并联机构的标定技术

运动学标定就是对实际几何参数的估计过程,通过标定来减少动平台的位姿误差。本文对并联机构的标定技术进行了介绍,并对一种四自由度并联机构的标定过程以两种标定方法进行了说明,给出了仿真结果。

Line Kinematics for Whole-Arm Manipulation

A Whole-Arm Manipulator uses every surface to both sense and interact with the environment. To facilitate the analysis and control of a Whole-Arm Manipulator, line geometry is used to describe the location and trajectory of the links. Applications of line kinematics are described and implemented on the MIT Whole-Arm Manipulator (WAM-1).

Minimizing Residual Vibrations in Flexible Systems

Residual vibrations degrade the performance of many systems. Due to the lightweight and flexible nature of space structures, controlling residual vibrations is especially difficult. Also, systems such as the Space Shuttle remote Manipulator System have frequencies that vary significantly based upon configuration and loading. Recently, a technique of minimizing vibrations in flexible structures by command input shaping was developed. This document presents research completed in developing a simple, closed- form method of calculating input shaping sequences for two-mode systems and a system to adapt the command input shaping technique to known changes in system frequency about the workspace. The new techniques were tested on a three-link, flexible manipulator.

Planning and Teaching Compliant Motion Strategies

This thesis presents a new high level robot programming system. The programming system can be used to construct strategies consisting of compliant motions, in which a moving robot slides along obstacles in its environment. The programming system is referred to as high level because the user is spared of many robot-level details, such as the specification of conditional tests, motion termination conditions, and compliance parameters. Instead, the user specifies task-level information, including a geometric model of the robot and its environment. The user may also have to specify some suggested motions. There are two main system components. The first component is an interactive teaching system which accepts motion commands from a user and attempts to build a compliant motion strategy using the specified motions as building blocks. The second component is an autonomous compliant motion planner, which is intended to spare the user from dealing with "simple" problems. The planner simplifies the representation of the environment by decomposing the configuration space of the robot into a finite state space, whose states are vertices, edges, faces, and combinations thereof. States are inked to each other by arcs, which represent reliable compliant motions. Using best first search, states are expanded until a strategy is found from the start state to a global state. This component represents one of the first implemented compliant motion planners. The programming system has been implemented on a Symbolics 3600 computer, and tested on several examples. One of the resulting compliant motion strategies was successfully executed on an IBM 7565 robot manipulator.

Compliance and Force Control for Computer Controlled Manipulators

Compliant motion occurs when the manipulator position is constrained by the task geometry. Compliant motion may be produced either by a passive mechanical compliance built in to the manipulator, or by an active compliance implemented in the control servo loop. The second method, called force control, is the subject of this report. In particular, this report presents a theory of force control based on formal models of the manipulator, and the task geometry. The ideal effector is used to model the manipulator, and the task geometry is modeled by the ideal surface, which is the locus of all positions accessible to the ideal effector. Models are also defined for the goal trajectory, position control, and force control.

The Design of a Mechanical Assembly System

This thesis describes a mechanical assembly system called LAMA (Language for Automatic Mechanical Assembly). The goal of the work was to create a mechanical assembly system that transforms a high-level description of an automatic assembly operation into a program or execution by a computer controlled manipulator. This system allows the initial description of the assembly to be in terms of the desired effects on the parts being assembled. Languages such as WAVE [Bolles & Paul] and MINI [Silver] fail to meet this goal by requiring the assembly operation to be described in terms of manipulator motions. This research concentrates on the spatial complexity of mechanical assembly operations. The assembly problem is seen as the problem of achieving a certain set of geometrical constraints between basic objects while avoiding unwanted collisions. The thesis explores how these two facets, desired constraints and unwanted collisions, affect the primitive operations of the domain.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

A Self-Organizing Neural Network Model for Redundant Sensory-Motor Control, Motor Equivalence, and Tool Use

A neural network is introduced which provides a solution of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory in 3-D space. To do this, the network self-organizes a mapping from motion directions in 3-D space to velocity commands in joint space. Computer simulations demonstrate that, without any additional learning, the network can generate accurate movement commands that compensate for variable tool lengths, clamping of joints, distortions of visual input by a prism, and unexpected limb perturbations. Blind reaches have also been simulated.

Neural Representations for Sensory-Motor Control I: Head-Centered 3-D Target Positions from Opponent Eye Commands

This article describes how corollary discharges from outflow eye movement commands can be transformed by two stages of opponent neural processing into a head-centered representation of 3-D target position. This representation implicitly defines a cyclopean coordinate system whose variables approximate the binocular vergence and spherical horizontal and vertical angles with respect to the observer's head. Various psychophysical data concerning binocular distance perception and reaching behavior are clarified by this representation. The representation provides a foundation for learning head-centered and body-centered invariant representations of both foveated and non-foveated 3-D target positions. It also enables a solution to be developed of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory in 3-D space.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

Effects of Constraint Errors on Parallel Manipulators with Decoupled Motion

From perspective of structure synthesis, certain special geometric constraints, such as joint axes intersecting at one point or perpendicular to each other, are necessary in realizing the end-effector motion of kinematically decoupled parallel manipulators (PMs) along individual motion axes. These requirements are difficult to achieve in the actual system due to assembly errors and manufacturing tolerances. Those errors that violate the geometric constraint requirements are termed “constraint errors”. The constraint errors usually are more troublesome than other manipulator errors because the decoupled motion characteristics of the manipulator may no longer exist and the decoupled kinematic models will be rendered useless due to these constraint errors. Therefore, identification and prevention of these constraint errors in initial design and manufacturing stage are of great significance. In this article, three basic types of constraint errors are identified, and an approach to evaluate the effects of constraint errors on decoupling characteristics of PMs is proposed. This approach is illustrated by a 6-DOF PM with decoupled translation and rotation. The results show that the proposed evaluation method is effective to guide design and assembly.

Workspace evaluation of manipulators through finite-partition of SE(3)

Workspace analysis and optimization are important in a manipulator design. As the complete workspace of a 6-DOF manipulator is embedded into a 6-imensional space, it is difficult to quantify and qualify it. Most literatures only considered the 3-D sub workspaces of the complete 6-D workspace. In this paper, a finite-partition approach of the Special Euclidean group SE(3) is proposed based on the topology properties of SE(3), which is the product of Special Orthogonal group SO(3) and R^3. It is known that the SO(3) is homeomorphic to a solid ball D^3 with antipodal points identified while the geometry of R^3 can be regarded as a cuboid. The complete 6-D workspace SE(3) is at the first time parametrically and proportionally partitioned into a number of elements with uniform convergence based on its geometry. As a result, a basis volume element of SE(3) is formed by the product of a basis volume element of R^3 and a basis volume element of SO(3), which is the product of a basis volume element of D^3 and its associated integration measure. By this way, the integration of the complete 6-D workspace volume becomes the simple summation of the basis volume elements of SE(3). Two new global performance indices, i.e., workspace volume ratio Wr and global condition index GCI, are defined over the complete 6-D workspace. A newly proposed 3 RPPS parallel manipulator is optimized based on this finite-partition approach. As a result, the optimal dimensions for maximal workspace are obtained, and the optimal performance points in the workspace are identified.

An intelligent configuration-sampling based local motion planner for robotic manipulators

Manipulator motion planning is a task which relies heavily on the construction of a configuration space prior to path planning. However when fast real-time motion is needed, the full construction of the manipulator's high-dimensional configu-ration space can be too slow and expensive. Alternative planning methods, which avoid this full construction of the manipulator's configuration space are needed to solve this problem. Here, one such existing local planning method for manipulators based on configuration-sampling and subgoal-selection has been extended. Using a modified Artificial Potential Fields (APF) function, goal-configuration sampling and a novel subgoal selection method, it provides faster, more optimal paths than the previously proposed work. Simulation results show a decrease in both runtime and path lengths, along with a decrease in unexpected local minimum and crashing issues.

Self-Feedback Motion Control for Cable-Driven Parallel Manipulators

This article proposes a closed-loop control scheme based on joint-angle feedback for cable-driven parallel manipulators (CDPMs), which is able to overcome various difficulties resulting from the flexible nature of the driven cables to achieve higher control accuracy. By introducing a unique structure design that accommodates built-in encoders in passive joints, the seven degrees of freedom (7-DOF) CDPM can obtain joint angle values without external sensing devices, and it is used for feedback control together with a proper closed-loop control algorithm. The control algorithm has been derived from the time differential of the kinematic formulation, which relates the joint angular velocities to the time derivative of cable lengths. In addition, the Lyapunov stability theory and Monte Carlo method have been used to mathematically verify the self-feedback control law that has tolerance for parameter errors. With the aid of co-simulation technique, the self-feedback closed-loop control is applied on a 7-DOF CDPM and it shows higher motion accuracy than the one with an open-loop control. The trajectory tracking experiment on the motion control of the 7-DOF CDPM demonstrated a good performance of the self-feedback control method.