Impedance mismatch: some differences between the way humans and robots control interaction forces

In over forty years of research robots have made very little progress still largely confined to industrial manufacture and cute toys, yet in the same period computing has followed Moores Law where the capacity double roughly every two years. So why is there no Moores Law for robots? Two areas stand out as worthy of research to speedup progress. The first is to get a greater understanding of how human and animal brains control movement, the second to build a new generation of robots that have greater haptic sense, that is a better ability to adapt to the environment as it is encountered. A remarkable property of the cognitive-motor system in humans and animals is that it is slow. Recognising an object may take 250 mS, a reaction time of 150 mS is considered fast. Yet despite this slow system we are well designed to allow contact with the world in a variety of ways. We can anticipate an encounter, use the change of force as a means of communication and ignore sensory cues when they are not relevant. A better understanding of these process has allowed us to build haptic interfaces to mimic the interaction. Emerging from this understanding are new ways to control the contact between robots, the user and the environment. Rehabilitation robotics has all the elements in the subject to not only enable and change the lives of people with disabilities, but also to facilitate revolution change in classic robotics.

Decentralized PID self-tuning control of industrial robots

A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

Results of applying sensor fusion to a control system using optic flow

The results from applying a sensor fusion process to an adaptive controller used to balance all inverted pendulum axe presented. The goal of the sensor fusion process was to replace some of the four mechanical measurements, which are known to be sufficient inputs for a linear state feedback controller to balance the system, with optic flow variables. Results from research into the psychology of the sense of balance in humans were the motivation for the investigation of this new type of controller input. The simulated model of the inverted pendulum and the virtual reality environments used to provide the optical input are described. The successful introduction of optical information is found to require the preservation of at least two of the traditional input types and entail increased training till-le for the adaptive controller and reduced performance (measured as the time the pendulum remains upright)

Investigation into the performance of an algorithm for deriving concise descriptions of optic flow fields for real-time control

This paper describes a region-based algorithm for deriving a concise description of a first order optical flow field. The algorithm described achieves performance improvements over existing algorithms without compromising the accuracy of the flow field values calculated. These improvements are brought about by not computing the entire flow field between two consecutive images, but by considering only the flow vectors of a selected subset of the images. The algorithm is presented in the context of a project to balance a bipedal robot using visual information.

Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.

Robots can't really be intelligent can they?

The whole concept of just what is and what is not, intelligence is a vitally important one. As humans interact more with machines, so the similarities and differences between human and machine intelligence need to be looked at in a sensible, scientific way. This paper considers human and machine intelligence and links them closely to physical characteristics, as exhibited by robots. Potential interfaces between humans and machines are also considered, as is the state of the art in direct physical links between humans and machines.

Path planning of robots in noisy workspaces using learning automata

The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.

From saccades to smooth pursuit: real-time gaze control using motion feedback

The authors present an active vision system which performs a surveillance task in everyday dynamic scenes. The system is based around simple, rapid motion processors and a control strategy which uses both position and velocity information. The surveillance task is defined in terms of two separate behavioral subsystems, saccade and smooth pursuit, which are demonstrated individually on the system. It is shown how these and other elementary responses to 2D motion can be built up into behavior sequences, and how judicious close cooperation between vision and control results in smooth transitions between the behaviors. These ideas are demonstrated by an implementation of a saccade to smooth pursuit surveillance system on a high-performance robotic hand/eye platform.