Анимация трехмерных объектов

В  данной  работе  рассматриваются  методы  инверсной  кинематики,  а  также  рассматривается модифицированный  итерационно‐численный  метод  вычисления  новых  положений  цепей  инверсной кинематики для каждого кадраIn  this  article  the  methods  of  inverse  kinematics  are  discusses.  A  modified  iteration‐numerical  method  for  calculating the new provisions of inverse kinematics chains for each frame is developed.

Scripting human animations in a virtual environment

The current deficiencies of virtual environment (VE) are well known: annoying lag time in drawing the current view, drastically simplified environments to reduce that time lag, low resolution and narrow field of view. Animation scripting is an application of VE technology which can be carried out successfully despite these deficiencies. The final product is a smoothly moving high resolution animation displaying detailed models. In this system, the user is represented by a human computer model with the same body proportions. Using magnetic tracking, the motions of the model's upper torso, head and arms are controlled by the user's movements (18 degrees of freedom). The model's lower torso and global position and orientation are controlled by a spaceball and keypad (12 degrees of freedom). Using this system human motion scripts can be extracted from the user's movements while immersed in a simplified virtual environment. Recorded data is used to define key frames; motion is interpolated between them and post processing adds a more detailed environment. The result is a considerable savings in time and a much more natural-looking movement of a human figure in a smooth and seamless animation

Adaptive perturbation control with feedforward compensation for robot manipulators

An adaptive perturbation control can track a time-based joint trajectory as closely as possible for all times over a wide range of manipulator motion and payloads. The adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The highly coupled nonlinear dynamic equations of a manipulator are expanded in the vicinity of a nominal trajectory to obtain the perturbation equations. The controlled system is characterized by feedforward and feedback components which can be computed separately and simulta neously. Given the joint trajectory set points, the feedforward component computes the corresponding nominal torques from the Newton-Euler equations of motion to compensate for all the interactions between joints. The feedback component, consisting of recursive least square identification and an optimal adaptive self-tuning control algorithm for the linearized system, computes the perturbation torques which reduce the position and veloc ity errors of the manipulator along the nominal trajectory. Because of the parallel structure, computations of the adaptive control may be implemented in low-cost microprocessors. This adaptive control strategy reduces the manipulator control prob lem from a nonlinear control to controlling a linear control system about a desired trajectory. Computer simulation results demonstrated its applicability to a three-joint PUMA robot arm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68750/2/10.1177_003754978504400303.pd

Fuzzy logic combined with neural algorithm to control industrial robot

The problem of finding the optimal path for the robot arm is one of the most important problems of industrial robot. Problem consists when robot looking for specific routes that require the lowest power consumption. Path that established between any two end points, can follow many paths. All these paths require different amounts of energy depending on the distance, velocity and acceleration. Path planning for robotic arms have a several degrees of freedom. This problem is solved by using neuro-fuzzy techniques. Using analytical and numerical techniques is very difficult to find a good solution. Mathematically is more difficulty to move a robotic arm in the presence of obstacles, but child instinctively moving his hand in the presence of obstacles. A way that allows us to progress is a neuro-fuzzy fusion systems. Neural networks make the ability to learn, while Fuzzy logic is based on the emulation of thinking of an expert. In addition, as hardware technology advances, more and more value will be placed on solutions that can be used in parallel processing, such as neural networks and fuzzy logic with neural networks

Fuzzy logic combined with neural algorithm to control industrial robot

The problem of finding the optimal path for the robot arm is one of the most important problems of industrial robot. Problem consists when robot looking for specific routes that require the lowest power consumption. Path that established between any two end points, can follow many paths. All these paths require different amounts of energy depending on the distance, velocity and acceleration. Path planning for robotic arms have a several degrees of freedom. This problem is solved by using neuro-fuzzy techniques. Using analytical and numerical techniques is very difficult to find a good solution. Mathematically is more difficulty to move a robotic arm in the presence of obstacles, but child instinctively moving his hand in the presence of obstacles. A way that allows us to progress is a neuro-fuzzy fusion systems. Neural networks make the ability to learn, while Fuzzy logic is based on the emulation of thinking of an expert. In addition, as hardware technology advances, more and more value will be placed on solutions that can be used in parallel processing, such as neural networks and fuzzy logic with neural networks

End-effector position analysis using forward kinematics for 5 DOF Pravak robot arm

Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major issue. This paper involves the kinematic analysis of a Pravak Robot arm which is used for doing successful robotic manipulation task in its workspace. The Pravak Robot Arm is a 5-DOF robot having all the joints revolute. The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. In this study the Denavit- Hartenberg (D-H) model is used to model robot links and joints. Pravak Robot Arm is a simple and safe robotic system designed for laboratory training and research applications. This robot allows to gain theoretical and practical experience in robotics, automation and control systems. The MATLAB R2007 is used to analyse end effectors position for a set of joint parameter

Sensors: A Key to Successful Robot-Based Assembly

Computer controlled robots offer a number of significant advantages in manufacturing and assembly tasks. These include consistent product reliability and the ability to work in harsh environments. The programmable nature of robotic automation allows the possibility of applying them to a number of tasks. In particular, significant savings can be expected in batch production, if robots can be applied to produce numbers of products successfully without plant re-tooling. Unfortunately, despite considerable progress made in robot programming [Lozano-Perez 83] [Paul 81] ;Ahmad 84] [Graver et al. 84] [Bonner & Shin 82] and in sensing [Gonzalez & Safabakhsh 82] [Fu 82] [Hall et al. 82], [Goto et al. 80], [Hirzinger & Dietrich 86], [Harmon 84], kinematics and control strategies [Whitney 85] [Luh S3] [Lee 82], a number of problems still remain unsolved before en-mass applications take place. In fact, in current applications, the specialized tooling for manufacturing a particular product may make up as much as 80% of the production line cost. In such a production line the robot is often used only as a programmable parts transfer device. Improving robots ability to sense and adapt to different products or environments so as to handle a larger variety of products without retooling is essential. It is just as important to be able to program them easily and quickly, without requiring the user to have a detailed understanding of complex robot programming languages and control schemes such as RCCL [Hayward & Paul 84], VAL-II [Shimano et al., 84], AML [Taylor et al., 83], SR3L-90 [Ahmad 84] or AL [Mujtaba & Goldman 79]. Currently there are a number of Computer Aided Design (CAD) packages available which simplify the robot programming problem. Such packages allow the automation system designer to simulate the assembly workcell which may consist of various machines and robots. The designer can then pick the motion sequences the robot has to execute in order to achieve the desired assembly task. This is done by viewing the motions on a graphical screen from different viewing angles to check for collisions and to ensure the relative positioning is correct, much the same way1 as it is done in on-line teach playback methods (see Figure 1). Off-line robot programming on CAD stations does not always lead to successful results due to two reasons: (i) The robot mechanism is inherently inaccurate due to incorrect kinematic models programmed in their control system [Wu 83] [Hayati 83] [Ahmad 87] [Whitney et ■ al. 84]. (ii) The assembly workcell model represented in the controller is not accurate. As a result parts and tools are not exactly located and their exact position may vary. This causes a predefined kinematic motion sequence program to fail, as it cannot deal with positional uncertainties. Sensors to detect real-time errors in the part and tool positions are obviously required with tailored sensor-based motion strategies to ensure assembly accomplishment. In this chapter we deal with how sensors are used to successfully ensure assembly task accomplishment. We illustrate the use of various sensors by going through an actual assembly of an oil pump. Additionally we illustrate a number of motion strategies which have been developed to deal with assembly errors. Initially, we discuss a number of sensors found in typical robotic assembly systems in Section 1. In Section 2 we discuss how and when sensors are to be used during an assembly operation. Issues relating to sensing and robust assembly systems are discussed very briefly in Section 3. Section 4 details a sensor-based robot assembly to illustrate practical applications

Robotics techniques for controlling computer animated figures

Thesis (M.S.V.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1986.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.Bibliography: leaves 88-92.by Alejandro José Ferdman.M.S.V.S