Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

RAPP System Architecture

International audience— Robots are fast becoming a part of everyday life. This rise can be evidenced both through the public news and announcements, as well as in recent literature in the robotics scientific communities. This expanding development requires new paradigms in producing the necessary software to allow for the users' particular needs. In this paper we present a novel architectural design of the RAPP framework that attempts to address this issue, developed within the context of the EU funded project RAPP "Robotic Applications for Delivering Smart User Empowering Application". The proposed framework has been designed aiming towards a cloud-based approach to integrating robotic devices and their respective applications. This goal was defined going beyond the up-coming trends in infrastructures, and focusing on alternative approaches to conventional robotic controllers, while at the same time expanding the capabilities of the RAPP framework in a seamless and scaling manner

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Génération automatique de résumés par analyse sélective

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Towards a Unified Approach to Learning and Adaptation

The aim of this thesis is to develop a system that enables autonomous and situated agents to learn and adapt to the environment in which they live and operate. In doing so, the system exploits both adaptation through learning and evolution. A unified approach to learning and adaptation, which combines the principles of neural networks, reinforcement learning and evolutionary methods, is used as a basis for the development of the system. In this regard, a novel method, called Evolutionary Acquisition of Neural Topologies (EANT), of evolving the structures and weights of neural networks is developed. The method introduces an efficient and compact genetic encoding of a neural network onto a linear genome that encodes the topology of the neural network implicitly in the ordering of the elements of the linear genome. Moreover, it enables one to evaluate the neural network without decoding it. The presented genetic encoding is complete in that it can represent any type of neural network. In addition to this, it is closed under both structural mutation and a specially designed crossover operator which exploits the fact that structures originating from some initial structure have some common parts. For evolving the structure and weights of neural networks, the method uses a biologically inspired meta-level evolutionary process where new structures are explored at larger timescale and existing structures are exploited at smaller timescale. The evolutionary process starts with networks of minimal structures whose initial complexity is specified by the domain expert. The introduction of neural structures by structural mutation results in a gradual increase in the complexity of the neural networks along the evolution. The evolutionary process stops searching for the solution when a solution with the necessary minimum complexity is found. This enables EANT to find optimal neural structures for solving a given learning task. The efficiency of EANT is tested on couple of learning tasks and its performance is found to be very good in comparison to other systems tested on the same tasks

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein