It is standard now in undergraduate and graduate courses in robotics to teach the basic concepts of position control design strategies. Due to the geared motors inherent in most educational and industrial manipulators, sophisticated control design strategies such as the inverse dynamics technique cannot be easily demonstrated in a laboratory setting. A direct drive 5-bar-linkage manipulator with reduced motor torque requirements is proposed in this paper for such a purpose. The manipulator dynamics are easily understood by undergraduates and an inverse dynamics control strategy is suggested which can be easily designed by students at the undergraduate level.

This paper describes an autonomous robot vehicle which can navigate through an initially unknown obstacle field to a nominated goal or systematically map its working environment. The navigation system uses combined ultrasonic beacon/odometry based localisation, optical range finders for environmental mapping, an A path planning procedure and continuous motion control. The computational support is divided between a graphics workstation 'home base' and a PC hosted transputer array on-board. The integration of all the subsystems cited above has been achieved and many successful navigation experiments completed. Possible further developments which would enhance the capabilities of the system are also discussed.

In this paper, we propose a robust controller for the tracking of robot motion. This controller is a nonlinear-based controller that compensates for the uncertainties present in the manipulator dynamic equation. The main result of this paper is that we explicitly show how the response of the tracking error can be modified by adjusting the control parameters. The corresponding stability result for the tracking error is Global Exponential Stability (GES). We then illustrate how similar control approaches are related to the proposed controller. Finally, simulation and experimental results are utilized to illustrate the performance of the robust controller.

Robots in the 20th century have been valued mainly for their enhancement of productivity. According to Professor Ichiro Kato of Waseda University, 21st-century robots will be valued because they enhance amenity. The development of robot systems for health care, detailed in this special issue of Robotica, is an affirmation of this forecast.

Several factors have combined to cause this development, although economic factors are paramount. Industrialised countries, where robots are most likely to be used, face an ageing population-a result of lower birth rates, reduced infant mortality and increased life expectancy. This ‘ageing society' is expected to have enormous impact upon these countries’ health and social security systems which devote a large proportion of their resources to the care of older people.

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.

This paper surveys the scientific literature on present and future applications of robotics to health care. The term ‘health care’ refers to different forms of assistance rendered to people who are unable without such assistance to perform physical tasks that ‘healthy’ people accomplish unaided. Research efforts and accomplishments to date are concentrated in four main application areas: rehabilitation; patient transfer; assistance to surgery; and ‘microrobots’ suitable for use inside people. Some initial work has been done in other areas including robotic diagnosis and therapy.

A mapping and navigation system based on certainty grids for an autonomous mobile robot operating in unknown environment is described. The system uses sonar range data to build a map of the robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world by experiment.

A general method is presented to automatically determine the placement of manipulators which allows one to optimize multiple kinematic performances indices during the execution of their tasks. It considers the presence of obstacles in the workstation and constraints on the motion of the manipulator's joints. The complete formulation is included, and an example with a six-degree-of-freedom manipulator in a cluttered environment es solved that demonstrates the improvements achieved for the manipulator performances by applying the method.

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 proposes a fast tracking error control method for a mobile robot with two differentially driven wheels. The tracking error between reference state and current state is transformed to the required displacement changes of each drive wheel by a wheel Jacobian. The major objective of this paper is to propose a control method for eliminating the tracking error quickly by controlling two independent driving wheels at the same time. To avoid long computational requirements of a Cartesian-based control, a kinematic model of the vehicle and co-ordinate system are introduced. Several simulation results are presented using this method. The fast tracking error control method proposed is mainly hardware-independent and Hence can be applied to various kinds of mobile robots which have two differentially driven wheels. The method was implemented on an experimental vehicle, WCVS, The experimentation shows a performance suitable for practical applications.

Continuing the work presented in part I, ‡ 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. We define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. We 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. Our results apply to both robots with and without maximum velocity limits.

A complication to the process of remotely generating tool paths is that mechanism controllers are typically capable of commanding a robot or machine tool to move along linear and circular arc space segments. This paper considers the decomposition of parametrically described, higher-order curvilinear paths into piecewise linear and circular path segments, thereby reducing the data required to store a higher order path.

A modeling method for robots is proposed, in which a convex polyhedron is represented as a set of inequalities and a robot is represented as a union of convex polyhedrons. With this method, collision between robots can be detected by solving a set of linear programming problems at every sampling instant. By detecting possible collision at every sampling instant, a directed graph for robots is created. The motion planning problem of robots is then transformed into a path searching problem in the directed graph and can be solved by exisiting searching algorithms.

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.

This paper describes the development of the topological and geometric approach to the analysis and synthesis of form-closed grasps of arbitrary objects. Concepts of wrench spaces and the relevant subsets of the positive cone, the affine hull, the convex huli, and the related polar cone are used, and applied to a representative example.

Even as the first industrial robot started earning its keep, researchers were dreaming of using robots in servicejobs, intimately serving human mentors as Isaac Asimovwould have had it. But that was not to be. Initially therewas a paucity of appropriate technology. Then the barriers became economic and institutional.

These barriers will be discussed to set the scene. The discussion then will focus on a spectrum of technologythat has been channelled into a cost-effective commercialproduct. HelpMate utilises an array of sensors andsensor fusion to succeed as a courier in multi-storeyedcluttered hospitals. Our advice for all researchers whowould have robots serve in health care is: “Get out intothe real world as soon as you can”.

Almost all industrial robot applications in use today are controlled using a control law that is simple and computationally efficient, local joint error feedback. When two or more open chain manipulators cooperate to manipulate the same object - such as in mechanical grippers, walking machines, and cooperating manipulator systems - closed kinematic chain, redundantly actuated mechanisms are formed. Control approaches for this type of system focus on the more computationally intensive computed torque or inverse plant control laws, due to the concern over instability caused by the unspecified distribution of control forces in the redundant actuator space, and due to the constrained motion caused by the closed kinematic chains.

It is shown that if one comes back to the formulation of the Hamilton's variational principle, it is then possible to obtain new viewpoints on the tracking control of robot manipulators. First, the Lagrange multiplier associated to the sliding surface can be interpretated in terms of control effort and/or forces of reaction of the mechanical system. Secondly, one can use the Taylor expansion of the mechanical Lagrangian, combined with a neighbour- ing Hamilton's principle, to obtain control schemes via sliding surfaces. Thirdly, a perturbation approach combined with the neighbouring Hamilton's principle provides results on the robustness of the control.

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.