Many real world problems quirea degree of flexibility that is to achieve using hand algorithms. One such domain is vision-based autonomous driving. In this task, the dual challenges of a constantly changing environment coupled with a real processing constrain the flexibility and of a machine learning system essential. This describes just such a learning system, called (Autonomous Land Vehicle In a Neural Network). It presents the neural network architecture and training techniques that allow to drive in a variety of including singlelane paved and unpaved roads, multilane lined and unlined roads, and obstacle-ridden on- and road environments, at speeds of up to 55 miles hour.

This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

The problem of driving an autonomous vehicle in normal traffic engages many areas of AI research and has substantial economic significance. We describe work in progress on a new approach to this problem that uses a decision-theoretic architecture using dynamic probabilistic networks. The architecture provides a sound solution to the problems of sensor noise, sensor failure, and uncertainty about the behavior of other vehicles and about the effects of one's own actions. We report on advances in the theory of inference and decision making in dynamic, partially observable domains. Our approach has been implemented in a simulation system, and the autonomous vehicle successfully negotiates a variety of difficult situations. 1 The BAT Project Several government agencies and corporations in Europe, Japan, and the US are currently undertaking research in IVHS (Intelligent Vehicle and Highway Systems) with the aim of substantially reducing congestion and accidents, which cost $500 billion/year...

A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multiagent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

This paper describes a method for detecting and recognizing road signs in gray-level and color images acquired by a single camera mounted on a moving vehicle. The method works in three stages. First, the search for the road sign is reduced to a suitable region of the image by using some a priori knowledge on the scene or color clues (when available) . Secondly, a geometrical analysis of the edges extracted from the image is carried out, which generates candidates to be circular and triangular signs. Thirdly, a recognition stage tests by cross-correlation techniques each candidate which, if validated, is classified according to the data-base of signs. An extensive experimentation has shown that the method is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways. A further improvement on the detection and recognition scheme has been obtained by means of temporal integration b...

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Many real world problems requireadegree of #exibility that is di#- cult to achieve using hand programmed algorithms. One such domain is vision-based autonomous driving. In this task, the dual challenges of a constantly changing environment coupled with a real time processing constrain make the #exibility and e#ciency of a machine learning system essential.

This paper deals with coordinating behaviour in a multi-autonomous robot system. When two or more autonomous robots must interact in order to accomplish some common goal, communication between the robots is essential. Different inter-robot communications strategies give rise to different overall system performance and reliability. After a brief consideration of some theoretical approaches to multiplerobot collections, we present concrete implementations of different strategies for convoy-like behaviour. The convoy system is based around two RWI B12 mobile robots and uses only passive visual sensing for inter-robot communication. The issues related to different communication strategies are considered.

RACCOON is a vision-based systen that tracks car taillights at night. It builds a global map in real time of the lead vehicle's position based on the location and separation of the taillights in a sequence of video images. RACCOON has been integrated into a car following experiment on the CMU Navlab II, a computer-controlled HMMWV testbed. The Navlab II safely followed a lead vehicle on a winding road in light tra#c at 32 km/h.

Robotic systems require the use of sensing to enable flexible operation in uncalibrated or partially calibrated environments. Recent work combining robotics with vision has emphasized an active vision paradigm where the system changes the pose of the camera to improve environmental knowledge or to establish and preserve a desired relationship between the robot and objects in the environment. Much of this work has concentrated upon the active observation of objects by the robotic agent. We address the problem of robotic visual grasping (eye-in-hand configuration) of static and moving rigid targets. The objective is to move the image projections of certain feature points of the target to effect a vision-guided reach and grasp. An adaptive control algorithm for repositioning a camera compensates for the servoing errors and the computational delays that are introduced by the vision algorithms. Stability issues along with issues concerning the minimum number of required feature points are discussed. Experimental results are presented to verify the validity and the efficacy of the proposed control algorithms. We then address an adaptation to the control paradigm that focuses upon the autonomous grasping of a static or moving object in the manipulator's workspace. Our work extends the capabilities of an eye-in-hand system beyond those as a "pointer" or a "camera orienter" to provide the flexibility required to robustly interact with the environment in the presence of uncertainty. The proposed work is experimentally verified using the Minnesota Robotic Visual Tracker (MRVT) [7] to automatically select object features, to derive estimates of unknown environmental parameters, and to supply a control vector based upon these estimates to guide the manipulator in the grasping of a...