Planning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment. In this paper we consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem. We consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lower bound on the probability. As these algorithms trade off plan time for plan quality, we compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose between these planning algorithms for other applications.

A method was developed that takes into account flexibility of robot links in the inverse dynamics calculations. This method uses the Newton-Euler equations and is applicable for special case systems that allow for only a small degree of flexibility. Application of the method should improve the accuracy of the position of the end effector during motion of the robot.

The results of this study show that the method can be based entirely on an existing rigid-link model with only minimal changes required as additions. The computational complexity of the method is discussed briefly as well and indicates an increase of computations of slightly more than a factor of two as compared to a rigid-link model for the same robot geometry.

The research results described present the performance of the Generalized Predictive Control (GPC) algorithm with a changing estimator and predictor model order for a specific application. The application is a hydraulically actuated heavy duty manipulator. Hydraulically actuated robotic manipulators, used in the large resource based industries, have a complex dynamic response in which, primarily due to the hydraulic actuator subsystems, the order of the dynamic model is not initially known and can change as the manipulator is operated. A nonlinear simulation model of the manipulator system is utilized in the work and the GPC controller is implemented with a CARIMA estimator together with an on-line, gradient based estimator model order determination technique. The results given show that with proper use of the order determination technique cost function and tuning of the GPC parameters, good performance and stability can be achieved.

Part I of this paper was concerned with kinematic workspaces of walking machines, while this paper addresses the static workspaces of a walking machine and their graphical representation. The results of static analysis are presented; the static workspace constraints are established; an algorithm for investigation of static workspaces is presented; and the position static workspaces are analysed and graphically represented for an example walking machine design.

Some changes have uncomfortable characteristics. Change is inevitable, large, penetrating, unpredictable. There is thus the need to create ‘learning’ organisations capable of coping with change. The following topics are considered:

1. Changes in organisational shape – transition from hierarchical triangular shaped to ‘heterarchical’ diamond shaped organisations – the breakdown hierarchy.

2. Changes in the role of people in manufacturing and services including the public service.

3. ‘Managers’ and new technology – management role enhanced co-ordinating and creating motivation.

4. Three models for change.

5. The productivity of automation is essential. ‘Working’ years are shortening, ‘dependent’ years are lengthening.

6. People as assets.

7. Technology, Human Aspects, Process of change = TRAP.

The paper presents an approach to automatic synthesis of program for robots in a flexible manufacturing cell (FMC). The system of program generation consists of two layers: Task-Level Programming Layer and Program Interpretation and Verification Layer. The first layer uses robot-independent planning techniques to create a work plan for robots (set of elementary actions) and program for each elementary action. The second layer uses robot-dependent planning methods to plan robot's trajectories and calculate the robot's motion times. A simulation model of whole FMC, which is created based on a description of FMC and program for robots, makes possible evaluation of efficiency of FMC work.

Learning in the age of information superhighway necessitates a properly-developed efficient vehicle that is not only powerful in directing users to the needed information or to situate in a reality through virtual settings, but also controllable at the various comfortable paces. The goal of this project is to explore a new on-line medium for users to navigate at their own pace in the structured cyberspace—knowledge space composed of concepts, systems design, application-oriented case studies, up-to-date industrial news (trends and product review), and on-line robotic systems, and to use it as a robotics work-bench for conducting controllable experiments/simulations. Through such an electronic learning medium, users will be able to acquire a global outlook as well as an integrated understanding of modern robotics in a manner that is low-cost, time-and-place-free, and student-centered.

The paper present a model of the kinematics of a rotary, redundant manipulator, in the form of a Finite State Machine, this is in fact, an example of AI production systems. This model is able to supply us with succesive configurations, calculated immediately in Cartesian space and allowing at the same time to considerably simplify the computations engaged in the graph searching. For an automaton-type model of the manipulator kinematics, diverse strategies of searching for a collision-free trajectory, reduced to a search of an appropriate path in the state-transition graph of FSM, are analyzed.

Collision-Avoidance is a key issue in planning trajectories for dual robots whose workspaces overlap. In this paper, we develop a new trajectory planning method by proposing a traffic control schemes. The traffic controller determines the next positions for each robot based on the motion priority and path direction subject to the collision-avoidance conditions and the robots' physical limits. The problem of determining the next positions is formulated and optimized. Algebraic expressions for collision avoidance between every-pair of links – one from the first robot and the other from the second robot – are derived in configuration space. These algebraic expressions are then used to solve the problem of determining “optimal” (in the sense of path direction and motion priority) robots' trajectories. A solution procedure is developed using a nonlinear programming (NLP) solver. The main advantage of our approach is that the two robots' trajectories can be determined simultaneously without requiring any a priori path information. Several numerical examples are presented to demonstrate the validity and effectiveness of the proposed approach.