A new real-time obstacle avoidance approach for mobile robots has been developed and implemented. This approach permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advancing toward the target. The novelty of this approach, entitled the Virtual Force Field, lies in the integration of two known concepts: Certainty Grids for obstacle representation, and Potential Fields for navigation. This combination is especially suitable for the accommodation of inaccurate sensor data (such as produced by ultrasonic sensors) as well as for sensor fusion, and enables continuous motion of the robot without stopping in front of obstacles. This navigation algorithm also takes into account the dynamic behavior of a fast mobile robot and solves the "local minimum trap" problem. Experimental results from a mobile robot running at a maximum speed of 0.78 m/sec demonstrate the power of the proposed algorithm. 2 1.

This paper reviews key concepts of the Autonomous Robot Architecture (AuRA). Its structure, strengths, and roots in biology are presented. AuRA is a hybrid deliberative/reactive robotic architecture that has been developed and refined over the past decade. In this article, particular focus is placed on the reactive behavioral component of this hybrid architecture. Various real world robots that have been implemented using this architectural paradigm are discussed, including a case study of a multiagent robotic team that competed and won the 1994 AAAI Mobile Robot Competition. 1 Introduction The Autonomous Robot Architecture (AuRA) was developed in the mid-1980's as a hybrid approach to robotic navigation [6]. Hybridization arises from the presence of two distinct components: a deliberative or hierarchical planner, based on traditional artificial intelligence techniques; and a reactive controller, based upon schema theory [2]. It was the first robot navigational system to be presented ...

We describe an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify these landmarks and measure their bearings relative to each other. Given such noisy input, the algorithm estimates the robot's position and orientation with respect to the map of the environment. The algorithm makes efficient use of our representation of the landmarks by complex numbers. The algorithm runs in time linear in the number of landmarks. We present results of simulations and propose how to use our method for robot navigation.

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This article describes techniques that have been developed for spatial learning in dynamic environments and a mobile robot system, ELDEN, that integrates these techniques for exploration and navigation in dynamic environments. In this research, we introduce the concept of adaptive place networks, incrementally-constructed spatial representations that incorporate variable-confidence links to model uncertainty about topological adjacency. These networks guide the robot's navigation while constantly adapting to any topological changes that are encountered. ELDEN integrates these networks with a reactive controller that is robust to transient changes in the environment and a relocalization system that uses evidence grids to recalibrate dead reckoning. Footnotes 1 Manuscript received . . . 2 Department of Computer Engineering and Science, Case Western Reserve University, Cleveland, OH, 44106, USA (email: yamauchi@alpha.ces.cwru.edu, URL: http://yuggoth.ces.cwru.edu/yamauchi/index.ht...

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A general approach to the local navigation problem for autonomous mobile robots (AMRs) and its application to omnidirectional and conventionally steered wheelbases are presented. The prob- lem of driving an AMR to a goal in an unknown environment is formulated as a dynamic feedback control problem in which local feedback information is used to make steering decisions while the AMR is moving. To obtain a computationally tractable algorithm, we propose a class of satisficing feedback strategies that generate reasonable, collision -free trajectories to the goal using simplified representations of the AMR dynamics and constraints. Realizations of the feedback strategy are presented and illustrated by simulation under the assumptions of perfect feedback information and zero servo error. Straight forward extensions of the approach to handle uncertainties in real systems are briefly described.

Opening new application areas for mobile robots requires to design systems that are robust, safe, and adaptive with respect to the environment they are operating in. Such systems must be smart enough to communicate with inexperienced users. They must therefore be easily programmable and understandable. This article describes PRIAMOS, an autonomous mobile robot equipped with a highly sophisticated sensor system which enables it to operate autonomously in a real office environment. Since the basis for efficient robot operation is a reliable model of the real world, particular interest is devoted to the PRIAMOS sensor system and the methods used to maintain the robot's world knowledge. A changing world requires changing strategies for problem solving. Approaches to the adaptation of PRIAMOS' task-related knowledge, i.e., to learning situation-action rules from experience, are presented. In that context, programming by demonstration and models of concept formation are considered to establ...

We present a complete architecture for behavioral control of locomotion for both real and simulated agents and provide a design methodology for building the locomotion control systems that embody the architecture. A low-level locomotion engine controls an agent's actions directly based on intermediate-level reactive behaviors such as attraction and avoidance. High-level state machines schedule and control the reactive behaviors allowing for more "intelligent" decision processes, and an agent model provides a mechanism for varying locomotion according to agent state and personality attributes. In addition to providing specifications for a locomotion engine, we address the problem of selecting and organizing an appropriate set of behaviors. We present selection criteria and a method for partitioning the behaviors to aid in implementation. We discuss the challenges specific to human locomotion and explain how to...

In this paper we describe an algorithm for fault tolerant sensor mapping for robotic vision. Basically we use a certainty grid algorithm to map distance measurements into a two-dimensional grid. The well-know certainty grid algorithm can tolerate occasional transient sensor errors and crash failures, but will fail when a sensor provides permanently faulty measurements. Therefore