Neural Network Based Workpiece Recognition And Robot Intelligent Grasping In Complicated Environment

Intelligent robot has the ability to perceive and acclimatize itself to the environment around it. So it is very different from conventional playback robot. Among the various senses of robot, vision is the most popular one. However, when robot works in complicated industrial environment, there are many disturbance factors to degrade the image signals, such as dust, variation of illumination, vibration of camera, shielding, noise in signal channel, etc. Besides these problems, the complicated and variable work environment makes it very difficult to design robot controller by conventional calibration technique. For these reasons, considering the sorting and griping manipulation of robot in the complicated industrial environment, this thesis discusses the problem of image restoration and recognition and visual servoing control for robot based on neural networks.Neural networks are herein the key technology permeating throughout this thesis for their abilities in nonlinear approximation, adaptation, generalization and associative memory, among which the complex-valued neural networks are capable naturally to process complex number, thus can be used to denote and process frequency signals directly, such as the image expressing in frequency domain.In order to solve the image degraded problem resulting from bad visual environment, complex-valued Hopfield neural network functioned as associative memory is utilized for image restoration in this thesis. Thus the analysis and synthesis of several kinds of complex-valued Hopfield neural networks are studied firstly. And based on such research results the image recognition and restoration are completed in complicated environment. This thesis also discusses the designation of visual servoing controller for robot by fuzzy behavior rules and neural network control. The main work in this thesis can be summarized as follows:1) Propose a synthesis method for a class of complex-valued discrete time Hopfield neural network, where the equilibrium condition and stability criterion are both satisfied. In this method, a local asymptotic stable condition is derived firstly and used to decide whether the solution of equilibrium equations is accepted. And if no, a gain-regulation of the neuron activation function is carried and guarantees finally both the stability and attractive ability for every storage patterns.2) Propose a synthesis method for a class of complex-valued continuous time Hopfield neural network under constrained attractive domain, which can satisfy the stable condition and equilibrium condition. In this method, this thesis constructs a Lyapunov function in complex-valued domain where the parameter of attractive domain is contained, and then analyses stability of the network. Considering two situations about time constants of the system, i.e. unknown or known, the corresponding solving algorithm is Pseudo-inverse and singular values decompose, respectively. The constraints of network parameters derived from equilibrium equations, together with the parameters of attractive domain, are denoted in the asymptotic stable condition which can be rewritten as linear matrix inequations. As a result, the networkè¿™"parameters can be solved easily and guarantee the storage patterns are both stable and attractive in the given attractive domain.3) Propose a synthesis method for a class of complex multi-valued Hopfield neural network used to express and process multi-valued information, for example, gray image where both the equilibrium condition and the requirement for energy function decreasing are considered. In this method a general solution of network equilibrium equation solved by singular value decomposition technique is adjusted according to the rule of decreasing the energy function. The final network weights can satisfy both stability and attraction, thus guarantee the reliable association memory of gray images.4) Study the image recognition for workpieces with different shape where the images are badly degraded by blur, defect or noise pollution because of the complicated industrial environment. The solution for degraded image recognition is to apply a class of complex-valued single layer perceptron. The proposed energy decreasing synthesis method by 3) for complex-valued Hopfield association and memory is utilized for restoring the degraded image, which can provide more reliable corner point information of the workpiece for the next grasping manipulation of robot.5) Propose a fuzzy behavior and neural network based visual servoing control scheme for intelligent grasping of the robot by the reason that the conventional visual servoing control method, i.e. image Jacobian matrix based method, has difficulties in modeling and its adaptive capacity is bad when robot works in a complicated and multivariate environment. The first phase of the scheme is fuzzy control which simulates the human control experience and be used to rough position for the clamp holder of the robot. And the second phase is neural network based control which applies the nonlinear approximation capability of BP network to model the nonlinear mapping from the image space to the joint space of the robot, and thus to complete the accurate position and orientation control of the clamp holder of robot.