Design Issues for Assistive Robotics for the Elderly

Q. Meng and M. H. Lee, Design Issues for Assistive Robotics for the Elderly, IARP / IEEE-RAS / Euron Workshop on Dependable Robots in Human Environments, Manchester, 2004.

Adapting Home Service Robot Behaviours by Experience Reuse and Interaction with Humans

Meng, Q., Lee, M. (2003). Adapting Home Service Robot Behaviours by Experience Reuse and Interaction with Humans. 673-678. Paper presented at IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Port Island, Kobe, Japan

Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot

T. G. Williams, J.J. Rowland, and Lee M.H., Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot, Proc. IEEE/RSJ Int. Conf. on Robots and Systems (IROS 2001), Nov., 2001, pp2300-2305.

How Useful is Lifelong Evolution for Robotics

Walker,J. and Wilson,M.S., 'How Useful is Lifelong Evolution for Robotics', Proceedings of the 7th International Conference on Simulation of Adaptive Behaviour, ed Hallam,B. and Floreano,D. and Hallam,J. and Hayes,G. and Meyer,J.A., pp 347-348, 2002, MIT Press

Evolving mobile robot behaviours in simulation -- a comparison

Walker,J. and Wilson,M.S., 'Evolving mobile robot behaviours in simulation -- a comparison', Towards Intelligent Mobile Robots; Proceedings of the 3rd annual British conference on autonomous mobile robotics and autonomous systems (TIMR'01), 2001

Endocrine Inspired Modulation of Artificial Neural Networks for Mobile Robotics

Sauze, C and Neal, M. 'Endocrine Inspired Modulation of Artificial Neural Networks for Mobile Robotics', Dynamics of Learning Behavior and Neuromodulation Workshop, European Conference on Artifical Life 2007, Lisbon, Portugal, September 10th-14th 2007.

Design issues for Assistive Robotics for the Elderly

Q. Meng and M.H. Lee, 'Design issues for Assistive Robotics for the Elderly', Advanced Engineering Informatics, 20(2), pp 171-186, 2006.

Biologically inspired automatic construction of cross-modal mapping in robotic eye/hand systems

Q. Meng and M.H. Lee, 'Biologically inspired automatic construction of cross-modal mapping in robotic eye/hand systems', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006,) ,4742-49, Beijing, 2006.

Intelligent Robotics

Lee, M.H., Intelligent Robotics, 224pp, Chapman and Hall, 1990.

Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior

Huelse, M., Wischmann, S., Manoonpong, P., Twickel, A.v., Pasemann, F.: Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior. In: M. Lungarella, F. Iida, J. Bongard, R. Pfeifer (Eds.) 50 Years of Artificial Intelligence, LNCS 4850, Springer, 186 - 195, 2007.

Diseño y fabricación de una tarjeta PCB (Shield) para el control de un robot autobalanceado basado en Arduino

Un robot autobalanceado es un dispositivo que, aun teniendo su centro de masas por encima del eje de giro, consigue mantener el equilibrio. Se basa o aproxima al problema del péndulo invertido. Este proyecto comprende el desarrollo e implementación de un robot autobalanceado basado en la plataforma Arduino. Se utilizará una placa Arduino y se diseñará y fabricará con un shield o tarjeta (PCB), donde se incluirán los elementos hardware que se consideren necesarios. Abarca el estudio y montaje del chasis y los sistemas de sensado, control digital, alimentación y motores

ImageRover: A Content-Based Image Browser for the World Wide Web

ImageRover is a search by image content navigation tool for the world wide web. To gather images expediently, the image collection subsystem utilizes a distributed fleet of WWW robots running on different computers. The image robots gather information about the images they find, computing the appropriate image decompositions and indices, and store this extracted information in vector form for searches based on image content. At search time, users can iteratively guide the search through the selection of relevant examples. Search performance is made efficient through the use of an approximate, optimized k-d tree algorithm. The system employs a novel relevance feedback algorithm that selects the distance metrics appropriate for a particular query.

Image Digestion and Relevance Feedback in the ImageRover WWW Search Engine

ImageRover is a search by image content navigation tool for the world wide web. The staggering size of the WWW dictates certain strategies and algorithms for image collection, digestion, indexing, and user interface. This paper describes two key components of the ImageRover strategy: image digestion and relevance feedback. Image digestion occurs during image collection; robots digest the images they find, computing image decompositions and indices, and storing this extracted information in vector form for searches based on image content. Relevance feedback occurs during index search; users can iteratively guide the search through the selection of relevant examples. ImageRover employs a novel relevance feedback algorithm to determine the weighted combination of image similarity metrics appropriate for a particular query. ImageRover is available and running on the web site.

Preferential Field Coverage Through Detour-Based Mobility Coordination

Controlling the mobility pattern of mobile nodes (e.g., robots) to monitor a given field is a well-studied problem in sensor networks. In this setup, absolute control over the nodes’ mobility is assumed. Apart from the physical ones, no other constraints are imposed on planning mobility of these nodes. In this paper, we address a more general version of the problem. Specifically, we consider a setting in which mobility of each node is externally constrained by a schedule consisting of a list of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged to achieve a specific coverage distribution of a field. Such a distribution defines the relative importance of different field locations. We define the Constrained Mobility Coordination problem for Preferential Coverage (CMC-PC) as follows: given a field with a desired monitoring distribution, and a number of nodes n, each with its own schedule, we need to coordinate the mobility of the nodes in order to achieve the following two goals: 1) satisfy the schedules of all nodes, and 2) attain the required coverage of the given field. We show that the CMC-PC problem is NP-complete (by reduction to the Hamiltonian Cycle problem). Then we propose TFM, a distributed heuristic to achieve field coverage that is as close as possible to the required coverage distribution. We verify the premise of TFM using extensive simulations, as well as taxi logs from a major metropolitan area. We compare TFM to the random mobility strategy—the latter provides a lower bound on performance. Our results show that TFM is very successful in matching the required field coverage distribution, and that it provides, at least, two-fold query success ratio for queries that follow the target coverage distribution of the field.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.