Image compression is essential for applications such as transmission of databases, etc. In this paper, we propose a new scheme for image compression combining recursive wavelet transforms with vector quantization. This method is based on the Kohonen Self-Organizing Maps (SOM) which take into account features of a visual system in both space and frequency domains.

One of the most important drawbacks of real robots is their lack of flexibility to changes in the environment or of the task. This paper presents a method enabling a visually guided robot to coordinate its movements with the vision processes. This method has been tested with a manipulator tracking maneuvering targets. The learning phase is performed in about one hour of repeated movements. After that, the robot can perform smooth and fast reaching movements and easily drop small objects into the wagon of a model train moving on an arbitrary trajectory.

In this paper, a force control algorithm for robot manipulators is introduced, where the dynamics of non-rigid environment interacting with the robot is assumed unknown. The controller design is based on the combination of sliding mode control techniques and the adaptive estimation theory, so the introduced controller compensates the structured or unstructured uncertainty of the environment. The main source of feedback information is received from a wrist force sensor. The designed controller includes additional absorption terms in order to minimise end-point velocity error and to suppress the impact effects at the beginning of the force application.

In this paper, the wall-following problem of an AGV is characterized by keeping a constant distance from a wall. Four Polaroid ultrasonic range sensors (sonar) are used to obtain information of the distance between the AGV and the wall. Four sonars are divided into two groups being placed at the front and back end on the right side of the AGV, respectively. A digital filter is used to get rid of disturbance and wrong readings. The former results mainly from the door and the latter from the specular reflection and accidental scattering. To obtain good dynamic and steady performances, a new compound fuzzy-I control algorithm is presented. Our methods have been verified by following a 15-meter-long wall with one door by using our TIT-1 AGV (TIT: Tianjin Institute of Technology). The results show a good performance with an error of ±2 cm from the desired distance to the wall.

The question of control and stabilization of flexible space robots is considered. Although, this approach is applicable to space robots of other configurations, for simplicity, a flexible planar two-link robot, mounted on a rigid floating platform, is considered. The robotic arm has two revolute joints and its links undergo elastic deformation in the plane of rotation. Based on nonlinear inversion technique, a control law is derived for controlling output variables describing the position and orientation of the platform and the joint angles of the robot. Although, the inverse controller accomplishes reference trajectory tracking, it excites the elastic modes of the arm. For the vibration suppression, three different stabilizers are designed. Using linear quadratic optimal control theory, a composite stabilizer for stabilization of the rigid and flexible modes and a decoupled flexible mode stabilizer are designed for regulating the end point of the robot to the target point and vibration suppression. Stabilization using only elastic mode velocity feedback is also considered. For large maneuvers, first the inverse controller is active, and the stabilizer is switched for regulation when the motion of the robot lies in the neighborhood of the terminal equilibrium state. Simulation results are presented to show that in the closed-loop system including the inverse controller and each of the stabilizers, trajectory tracking and stabilization of elastic modes are accomplished.

Optimal control of fingertip force during grasping operation by multifingered robotic end effectors is an important consideration. Determination of optimal fingertip force is, however, very complicated due to the involvement of a number of contact parameters at the finger-object interface including the mass of the object and the frictional properties of the contact surfaces. Modelling of various contact parameters is computationally overloading, which may not be tenable in practical situations where objects of different mass and material are available. Also for an unknown and unstructured environment, these properties may not be known in advance. This paper presents a controller based on fuzzy logic which is capable of performing optimal grasp of objects without knowing their mass and frictional properties. The controller also accounts for stability and dynamic aspects of the grasp. The experimental results of the implementation are presented.

The motion planner presented in this paper enables the user to design movements in an interactive way, and to integrate sensor signals efficiently. In addition to the usual movements bound to a path, which can now be combined like lego pieces, smooth and fast reactions to arbitrary changes of the target are also provided. This motion planner opens new possibilities for flexible and sensor-oriented robotics, as examples from its implementation on two robots show.

An adaptive control of multiple robot compliant manipulation of a dynamical environment is synthesized. It may be implemented when: (i) there is no good understanding of all physical effects incorporated in the multiple robots/object/environment system to be controlled; (ii) the parameters of the system are not precisely known, (iii) the system parameters do vary in a known region about their nominal values. The proposed adaptive control law has the inverse dynamics controller structure and is composed of an identification part (parameter update law), and a control law part. It is proved that the proposed adaptive controller is asymptotically stable. The simulation results verify the proposed approach to coordinated adaptive control of multiple robot manipulators in constrained motion tasks. They also verify that the multiple robots/object/environment system can track a step parameter change.