This paper presents a structured power and energy-flow-based qualitative modelling approach that is applicable to a variety of system types including electrical and fluid flow. The modelling is split into two parts. Power flow is a global phenomenon and is therefore naturally represented and analysed by a network comprised of the relevant structural elements from the components of a system. The power flow analysis is a platform for higher-level behaviour prediction of energy related aspects using local component behaviour models to capture a state-based representation with a global time. The primary application is Failure Modes and Effects Analysis (FMEA) and a form of exaggeration reasoning is used, combined with an order of magnitude representation to derive the worst case failure modes. The novel aspects of the work are an order of magnitude(OM) qualitative network analyser to represent any power domain and topology, including multiple power sources, a feature that was not required for earlier specialised electrical versions of the approach. Secondly, the representation of generalised energy related behaviour as state-based local models is presented as a modelling strategy that can be more vivid and intuitive for a range of topologically complex applications than qualitative equation-based representations.The two-level modelling strategy allows the broad system behaviour coverage of qualitative simulation to be exploited for the FMEA task, while limiting the difficulties of qualitative ambiguity explanation that can arise from abstracted numerical models. We have used the method to support an automated FMEA system with examples of an aircraft fuel system and domestic a heating system discussed in this paper.

Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.

In this paper, we describe a new error-driven active learning approach to self-growing radial basis function networks for early robot learning. There are several mappings that need to be set up for an autonomous robot system for sensorimotor coordination and transformation of sensory information from one modality to another, and these mappings are usually highly nonlinear. Traditional passive learning approaches usually cause both large mapping errors and nonuniform mapping error distribution compared to active learning. A hierarchical clustering technique is introduced to group large mapping errors and these error clusters drive the system to actively explore details of these clusters. Higher level local growing radial basis function subnetworks are used to approximate the residual errors from previous mapping levels. Plastic radial basis function networks construct the substrate of the learning system and a simplified node-decoupled extended Kalman filter algorithm is presented to train these radial basis function networks. Experimental results are given to compare the performance between active learning and passive learning

Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuro-science is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a color camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of infants during early cognitive development

This paper presents a robot vision system that is able to develop its vision gradually in its environment, from motion detection, to analysis of static features of objects, to object individuation, to identifying object unity, to tracking objects and finally to understanding some fundamental object relations. Through a developmental approach, our robot vision system is adaptive to environmental changes and follows autonomous self development.

We describe an approach to sensory-motor learning and coordination that draws from psychology rather than neuroscience. The growth of the motor coordination is controlled through sequential lifting of constraints, which is inspired by Jean Piaget's developmental learning theory. Our objective is the implementation of a flexible learning framework for an embodied hand/eye system which exhibits a prolonged epigenetic developmental process. The results show how staged competence can be shaped by qualitative behavior changes produced by anatomical, computational and maturational constraints.

In this paper, we describe the incorporation of learning and reuse of experience into behavior-based service robot systems. Experience is context based, and the learned experience is associated with its relevant behaviors. Object constraints are obtained by virtual object movements in image space. In addition to retaining the key properties of behavior-based systems, our approach also has the planning ability to recover from errors and to imitate an object pattern shown by humans. Experiments on a real physical robot system have successfully tested the approach.

We report the results of a programme of work that allows us to specify and execute robotic assembly of snack food products such as sandwiches and pizzas simply by presenting to the system hand-prepared examples of the desired results of the robot operations. We summarise a detailed experimental evaluation of our pick and place work, in which placement location and orientation are taught by example. We also describe and evaluate our recent work on automated spreading of ingredients so as to form specific surface profiles. Our methods facilitate rapid reconfiguration to accommodate new product variants without the need for reprogramming.

Sectors of the food processing industry have challenging requirements for automation: short batches, product innovation, variation in component shape, all with high production volume. Hard-programmed automation cannot provide the flexibility required. We report our initial work on an approach in which robot operations are derived from the sensed difference between a part-assembled product and a pre-sensed example of the result required. We describe a workcell with laser sensor and present results for experiments on simple assemblies of real sandwich and pizza components. The technique offers flexibility without reprogramming.

We deal with the application of fuzzy concepts directly to spatial representations in order to guide inferences based on sensed spatial parameters. Our context is exploratory mobile robotics and we describe a series of experiments on a Nomad 200 robot using a software simulator. We first present a fuzzy model of distance, alignment and sensing aperture and use this to interpret ultrasonic sensor data collected with varying degrees of noise. Wall-following is used as an example exploratory task and a non-fuzzy strategy is used as a control and to collect reference data. The design and implementation of a fuzzy map follower is then described and results are discussed. The fuzzy fusion of spatial data is shown to give markedly superior performance in dealing with noise and results show the fuzzy spatial method reduces cumulative position error by 80%. Certain situations can cause occasional overshoot and these are explained in terms of the spatial resolution of the method.

The aim of this work is to investigate behavioural control strategies inspired by 'perceptual control theory' demonstrated by Powers (1973), and their potential application in an industrial robotic handling task. In the particular approach reported here, an Adept robot is stationed over a conveyor belt which delivers the frozen food product pieces at random position and orientation. A vision system, placed upstream of the robot, senses the position and shape of individual product items. The robot grasps each product item and transports it to a packing location where it is placed into a box. Achievement of reliable grasping is made more difficult by shape variations within individual product types, by the need to accommodate, without software modifications, new shapes as new products are developed, and by the need to change from one product type to another with minimal operator involvement.

This paper describes a control method for searching suitable gripping points on the outline of generic shapes. It is aimed at robotic grasping tasks where no full geometrical knowledge of the shape is assumed. We first describe the method for a range of 2D generic shapes and then instantiate the method for pick and place operations on known shapes. The algorithms are run on the shape as it appears on the computer screen directly from a vision system. Virtual robotic fingers and associated sensors are then configured and positioned on the screen with three variables being simultaneously controlled. When the control systems reach a steady state, information about virtual finger position and orientation are then used to drive the manipulator.

This paper presents the initial development of a visually guided robotic system for handling food products that are often presented in an unstructured manner. In order to handle the communications between the host and robot controller more effectively, we developed multi-level commands within the host programming environment. Vision guided grasping is described in the context of vector manipulations. The system is able to calculate the grasp vector according to both online visual information and off-line data based on the food product and gripper type in consideration. A case study is presented where the system handles trapezoidal shaped fish products.>

Advanced robotic applications often require a robot to work as a part of an integrated system. Supervisory control of the robot in such cases readily permits incorporation of external sensing systems such as vision. To handle communication between a host and robot controller more effectively, we have developed multilevel commands within the host programming environment. An example application is presented which is a visually guided robot food handling task. The work cell comprises a robot manipulator, a vision system, and a host controller whereby the supervisory control of the robot is achieved.>