The Fourier series is employed to approximate the input/output (I/O) characteristics of a dynamic system and, based on the approximation, a new learning control algorithm is proposed in order to find iteratively the control input for tracking a desired trajectory. The use of the Fourier series approximation of I/O renders at least a couple of useful consequences: the frequency characteristics of the system can be used in the controller design and the reconstruction of the system states is not required. The convergence condition of the proposed algorithm is provided and the existence and uniqueness of the desired control input is discussed. The effectiveness of the proposed algorithm is illustrated by computer simulation for a robot trajectory tracking. It is shown that, by adding a feedback term in learning control algorithm, robustness and convergence speed can be improved.

We consider the problem of moving a point robot through a two dimensional workspace containing polygonal obstacles moving on unknown trajectories. We propose to use sensor information to predict the trajectories of the obstacles, and interleave path planning and execution. In this paper, we present preliminary work in which we propose our basic algorithm and define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. In the sequel (part II) to this paper we will show that the complexity of a path planning problem can be characterized by how frequently the robot must change directions to approximate the locally minimum velocity path.

This paper presents a trajectory generation method using smooth functions for an automatic guidance system of an autonomous vehicle with two differentially driven wheels within structured environments. A control algorithm based on an incrementally generated smooth trajectory gives a good performance when implemented on an experimental vehicle.

An analytic solution approach to the time-varying obstacle avoidance problem is adopted. The problem considers the collision between any link of the robotic manipulator and the time-varying obstacle. The information on the motion and shape change of the obstacle is given prior to robot motion planning. To facilitate the problem, we analyze and formulate it mathematically in a robot joint space. We then introduce the view-time concept and analyze its properties. Using the properties of the view-time, a view-time based motion planning method is proposed. The view-time based method plans the robot motion by units of the view-time. In every view-time, it uses a stationary obstacle avoidance scheme. The proposed method is applied to the motion planning of a 2 DOF robotic manipulator in an environment with a polyhedral moving obstacle.

This paper presents a sensory gripper, consisting of two tactile sensing matrices which acquire three dimensional images of objects of interest. The image processing algorithm uses elastostatic contact information to discriminate among a host of parts made of different materials. The algorithm also enables the assessment of orientation of parts without the pre-requisite of having to recognise them. The positions of stable holdsites and a safe gripping force are also evaluated.

Human posture is the co-operation of parts of the body to perform a task. This organization is self optimised by the operator to minimize strain and pain and maximize efficiency. This problem is formulated as a problem of inverse kinematic of a redundant robot. Constraints are imposed by the visual and manual task, and criteria may be modified in relation to operator motivation and tiredness. An interactive graphical software is designed to teach the postural problem and as an ergonomic aid.

Recent hardware advances for robot accessories include self changing grippers. A universal wrist has the capability of accessing and pneumatically attaching itself to a limited number of grippers that can be stored in a magazine. This paper addresses the determination of self changing gripper characteristics to permit the grasping of a wide variety of geometric shapes with a limited number of different gripper types.

The consideration of part motion instability is crucial to the determination of assembly sequence since this affects the complexity of fixtures and dexterity of robots. This paper presents a new approach to the inference of robotic assembly sequences, taking into consideration the instability of base assembly motions. Based upon the evaluation of motion instability, the method generates stable assembly sequences by use of the precedence constraint inference method previously developed by the authors. An example assembly of a 10-part electrical relay is given to illustrate the concepts and procedure of the proposed scheme. The results show that the proposed method can be effectively utilized to generate stable assembly sequences, thus providing crucial information on the fixture design and selection of types of assembly robots.

In this paper a control law, which consists of a fuzzy logic controller plus a nonlinear effects negotiator for a flexible robot manipulator, is presented. The nonlinear effects negotiator is used to enhence the control system's ability in dealing with the uncertainty of the mathematical model. The control algorithm is simple and easy to tune as opposed to conventional control law which requires time consuming gains selections. To obtain fuzzy control rules, an error response plane method is proposed.

Control laws are described for solving the task of stabilization of motion and force of interaction of a robot with its environment with a prescribed quality of transient processes with respect to position and in the presence of control, motion, and interaction force constraints. The robustness of these laws to parametric perturbations and their stability with respect to initial and external perturbations and measuring sensor errors have been proven.