The work demonstrates a new approach to design of a level of intelligent control of robotic systems. The analytical model results in operational decisions. The structure of these decisions make them readily available to be implemented as an expert system. The approach is applied to a case study of mobile supervisory robots. The model presented here was motivated by manufacturing robotic systems and a type of autonomous robots that collect information at different sites for safety and other control purposes. The robot actions are directly affected by the obta~ned data. At each site the amount of available information (and thus the correctness of the robot decision) can be increased if the robot keeps collecting data at that site for a longer period of t~me. This means a delay in reacting and in reaching next site and accordingly, a decrease in the general amount of robot's information on the whole system.

The method of finding an economic amount of information collected by a robot at each site is based on the theory of controlled discrete event stochastic systems developed in our earlier works. This theory combines he basic concepts of discrete event control extended to stochastic systems with some aspects of information economics.

In this paper a control strategy for a redundant PUMA robot is presented. This robot is obtained from the conventional PUMA robot by addition of a joint parallel to the elbow joint. For redundancy resolution the following approach is chosen. From the position and pointing direction of the end effector of the PUMA robot the position and orientation of the fourth link of a 4R-manipulato? is calculated and the redundancy is resolved for this manipulator. This is done by adding an equation to the relationship between the joint angles and position and orientation of the fourth link. By this approach a control strategy is derived that allows motions of the end effector of the PUMA robot in a large part of its work space and shows repeatable behaviour. Furthermore, the redundancy is utilized so that the fiexture of the wrist is kept small.

In this paper, a joint space dynamic control scheme with an adaptive identifier is proposed for free-flying space robots. The control in Cartesian space poses a measurement problem which is critical from a point of view of implementation. In order to overcome this problem, a joint space control is developed. An inverse kinematics algorithm is proposed so as to control free-flying space robots in joint space. Since the inverse kinematic solutions for space robots depend on the dynamic parameters as well as the kinematic.parameters, the accurate estimation of all the unknown parameters is essential to make joint space control possible. Therefore, an off-line adaptive parameter identification is performed for free-flying space robots. Simulation results are given to show the validity and the effectiveness of the presented adaptive identification and dynamic control scheme.

We examine the properties of an integrated, intelligent controller for multifingered manipulators. The discussion addresses the dynamic constraints on computation and resource allocation in systems which must continually adjust objectives and the set of behaviors employed to attain them. We present a means of specifying general force domain tasks and include techniques for reflexive, sensor-based control and for optimal combinatoric grasp synthesis as alternative grasp formation mechanisms. Examples are presented to illustrate the acquisition of information, the incremental refinement of models, the use of uncertain information in reflexive grasp formation, and the use of complete geometric information in optimal grasping.

This paper outlines the analysis methodology employed in the design of an anthropomorphic hand for the purposes of object handling by robotic manipulators, and for prosthetics. The final design is described and the safe handling of various objects using this anthropomorphic hand is discussed.

This paper presents an example of the application of geometric and algebraic approaches to the inverse kinematics problem of four-link robot manipulators. A special arm configuration of the robot manipulator is employed for solving the inverse kinematics problem by using the geometric approach. The obtained joint variables as angular positions are defined in the form of cubic polynomials. The other kinematic parameters of the joints, such as angular velocities and angular accelerations, are the time derivatives of these polynomials. It is evident that there is no definite difference between the results of the two approaches. Consequently, if an appropriate arm configuration for the geometric approach can be established, the inverse kinematics can be solved in a simpler and shorter way.

In this paper, a complete model of the elasto-kinematics is formulated in terms of a new kinematic notation, called the algebra of rotations. Based on this formulation, the elegant and concise expressions are derived for the displacement equation and especially the Jacobians governing the motion mapping between the manipulator tip and joint variables as well as link deflections. Introduction of the elasto-kinematics into the elasto-dynamics can directly take into consideration the nonlinear coupling between joint variables and link deflections, and thus improve the result of the elasto-dynamics.

Methods are described for computer analysis of image-data from a coded-stripe rangefinder. The main objective is to find vertex coordinates and connectivity information for a polyhedral object, enabling it to be represented by a wire-frame model. For each of several rangefinder viewpoints, the data is processed to extract three-dimensional edge and vertex positions. The emphasis is on estimation techniques that make good use of fairly sparse data-points. Results from different viewpoints are merged to produce a 3D model of the object.

This paper presents a qualitative theoretical formulation for synthesis and analysis of multiple contact dexterous manipulation of an object, using a robot hand. The motivation for a qualitative theory is to build a formalisation of ‘human-like’ common-sense reasoning in robotic manipulation. Using this formalisation, a robot hand can perform finger-tip manipulative movements by analysing the physical laws that govern the robot hand, the object, and their interaction. Traditionally, such analysis have been framed in quantitative terms leading to mathematical systems which become intractable very quickly. Also, quantitative synthesis and analysis, often demand an accurate specification of the parameters in the universe of discourse, which is almost impossible to provide. The qualitative approach inherently encounters both these problems successfully.

The qualitative theory is presented in three developmental stages. A qualitative framework of spatial information in the context of dexterous manipulation has been provided. Qualitative models of an object configuration and transformations in them that occur during a manipulation process, have been developed. Finally, the development of a ‘quasi-static’ qualitative framework of a dexterous manipulation process that performs the desired object transformation, has been presented.

In a measurement system with intelligent, distributed sensor processes, complementary observations from different sensor need be combined with each other. This paper describes a method based on fuzzy measures, in which a global ‘fusion algorithm’ questions the sensors as to their support and opinion of a hypothesis. The sensor opinions are clustered into groups based on their support of each others' opinions, and fused using a new fuzzy operator.

This paper describes an adaptive control system for an articulated robot with n joints carrying a variable load. The robot is a complex nonlinear time-varying MIMO plant with dynamic interaction between its inputs and outputs. However, the design of the control system is relatively straightforward and does not require any prior knowledge about the plant. This is because the control system is based on using neural networks which can capture the dynamic characteristics of the plant automatically. Three neural networks are employed in total, the first to learn the dynamics of the robot, the second to model its inverse dynamics and the third, a copy of the second neural network, to control the robot.

This paper presents an advanced control system for an active compliant device. This device, a manipulator-gripper, was designed to achieve stable grasp of objects with various shapes and to impart compliant fine motions to the grasped object. In the control system of this end-effector, we introduced autonomous reasoning capabilities. Fine motion strategies, needed for mating or grasping, use inductive learning from experiments to achieve uncertainty and error recovery. An overview of the articulated gripper's structure is provided for a better understanding of the programming environment we propose. For solving the problem of synthesis programs for fine motion planning we introduce declarative programming facilities in the controller through a time-sensitive mini-prolog. The paper gives some details on the implementation of this mini-prolog. We develop a heuristic procedure to obtain an implicit local model of contacts in complex assembly tasks. Finally, a specific example of this approach – a peg-in-hole operation– –is outlined.

A new vision system architecture has been developed to support the visual navigation of an autonomous mobile robot. This robot is primarily intended for urban park inspection, so it should be able to move in a complex unstructured environment. The system consists of various modules each ensuring a specific task involved in autonomous navigation. Task coordination focuses on the central module called the supervisor which triggers each module at the time appropriate to the current situation of the robot. Most of the processing time is spent with the scene exploration module which is based on the Hough transform to extract the dominant straight features. This module operates in two modes: the initial phase which forms the type of processing applied to the first image acquired in order to initiate navigation, and the continuous following mode which ensures the processing of subsequent images taken at the end of the blind distance. In order to rely less on visual data, a detailed map of the environment has been established, and an algorithm is used to make a scene prediction based on robot position provided by the localization system. The predicted scene is used to validate the objects detected by the knowledge base. This knowledge base uses the acquired and predicted data to construct a scene model which is the main element of the vision system.

This work concerns the class of wheeled mobile robots with single axis front steering. Because of the relatively low speed of these vehicles their dynamic properties have little effect on their path tracking behaviour. Their motion is, moreover, on a flat environment and can be assumed two dimensional.

The kinematics of motion of such a vehicle can be utilized for design of control strategies for maintaining path following stability. In this paper, optimal control technique is implemented to such a system. First, the state space equations governing the motion are derived. The orientation error and the offset from a path form the states and the steering angle is the control input to the system. An optimal controller to minimize the two errors and the steering angle is then sought. The conditions for the existence of the feedback matrix are discussed. The controller structure is defined in terms of the forward speed of the vehicle and, thus, has the advantage of being flexible for speed changes. Numerical illustrative examples, however, demonstrate that variation of the speed has no effect on the controller structure.