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The area of intelligent autonomous vehicles or robots has proved to be very active and extensive both in challenging applications as well as in the source of theoretical development. Automation technology is rapidly developing in many areas including: agriculture, mining, traditional manufacturing, automotive industry and space exploration. The 2nd IFAC Conference on Intelligent Autonomous Vehicles 1995 provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of developments in automation technologies.
Paperback. The area of intelligent autonomous vehicles or robots has proved to be very active and extensive both in challenging applications as well as in the source of theoretical development. Automation technology is rapidly developing in many areas including: agriculture, mining, traditional manufacturing, automotive industry and space exploration. The 2nd IFAC Conference on Intelligent Autonomous Vehicles 1995 provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of developments in automation technologies.
When human drivers let intelligent software take the wheel: the beginning of a new era in personal mobility.
This text presents the proceedings of a conference on intelligent autonomous systems. Papers contribute solutions to the task of designing autonomous systems that are capable of operating independently of a human in partially structured and unstructured environments. For specific application, these systems should also learn from their actions in order to improve and optimize planning and execution of new tasks.
There is an increasing range of applications in which a robot has to operate in large unstructured and uncertain environments - including military cross country missions, fire fighting, construction, nuclear plant inspections, inspecting and repairing subsea structures, assembling space stations, as well as in intelligent automobiles. Uncertainty dominates the problem domain for intelligent autonomous vehicles (IAVs) through sensing the environment and vehicle state, interpreting the data, assessing the situation, adapting to changes in the environment or tasking, replanning, navigation and piloting. IFAC, recognising the industrial, technical and economic significance of IAV research, established an International Working Party to promote research and dissemination of results in IAV systems. The IAV-93 Southampton Workshop and these resulting proceedings exemplify the vitality and significant progress made by leading IAV researchers worldwide.
This book covers the start-of-the-art research and development for the emerging area of autonomous and intelligent systems. In particular, the authors emphasize design and validation methodologies to address the grand challenges related to safety. This book offers a holistic view of a broad range of technical aspects (including perception, localization and navigation, motion control, etc.) and application domains (including automobile, aerospace, etc.), presents major challenges and discusses possible solutions.
Distributed autonomous robotic systems (DARS) are systems composed of multiple autonomous units such as modules, cells, processors, agents, and robots. Combination or cooperative operation of multiple autonomous units is expected to lead to desirable features such as flexibility, fault tolerance, and efficiency. The DARS is the leading established conference on distributed autonomous systems. All papers have the common goal to contribute solutions to the very demanding task of designing distributed systems to realize robust and intelligent robotic systems.
This thesis is concerned with the theoretical and practical development of reliable and robust localisation algorithms for autonomous land vehicles operating at high speeds in unstructured, expansive and harsh environments. Localisation is the ability of a vehicle to determine its position and orientation within an operating environment. The need for such a localisation system is motivated by the requirement of developing autonomous vehicles in applications such as mining, agriculture and cargo handling. The main drivers in these applications are for safety, efficiency and productivity. The approach taken to the localisation problem in this thesis guarantees that the safety and reliability requirements imposed by such applications are achieved. The approach also aims to minimise the engineering or modification of the environment, such as adding artificial landmarks or other infrastructure. This is a key driver in the practical implementation of a localisation algorithm. In pursuit of these objectives, this thesis makes the following principal contributions: 1. The development of an Iterative Closest Point - Extended Kalman Filter (ICP-EKF) algorithm - a map-based iconic algorithm that utilises measurements from a scanning laser rangefinder to achieve localisation. The ICP-EKF algorithm entails the development of a map-building algorithm. The main attraction of the map-based localisation algorithm is that it works directly on sensed data and thus does not require extraction and matching of features. It also explicitly takes into account the uncertainty associated with measurements and has the ability to include measurements from a variety of different sensors. 2. The development and implementation of an entropy-based metric to evaluate the information content of measurements. This metric facilitates the augmentation of landmarks to the ICP-EKF algorithm thus guaranteeing reliable and robust localisation. 3. The development and adaptation of a view-invariant Curvature Scale Space (CSS) landmark extraction algorithm. The algorithm is sufficiently robust to sensor noise and is capable of reliably detecting and extracting landmarks that are naturally present in the environment from laser rangefinder scans. 4. The integration of the information metric and the CSS and ICP-EKF algorithms to arrive at a unified localisation framework that uses measurements from both artificial and natural landmarks, combined with dead-reckoning sensors, to deliver reliable vehicle position estimates. The localisation framework developed is sufficiently generic to be used on a variety of other autonomous land vehicle systems. This is demonstrated by its implementation using field data collected from three different trials on three different vehicles. The first trial was carried out on a four-wheel drive vehicle in an underground mine tunnel. The second trial was conducted on a Load-Haul-Dump (LHD) truck in a test tunnel constructed to emulate an underground mine. The estimates of the proposed localisation algorithms are compared to the ground truth provided by an artificial landmark-based localisation algorithm that uses bearing measurements from a laser. To demonstrate the feasibility and reliability of both the natural landmark extraction and localisation algorithms, these are also implemented on a utility vehicle in an outdoor area within the University's campus. The results demonstrate the robustness of the proposed localisation algorithms in producing reliable and accurate position estimates for autonomous vehicles operating in a variety of unstructured domains.