Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a ne-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a ltering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described he...

ion and delegation: Agents can be made extensible and composable in ways that common iconic interface objects cannot. Because we can "communicate" with them, they can share our goals, rather than simply process our commands. They can show us how to do things and tell us what went wrong (Miller and Neches 1987). . Flexibility and opportunism: Because they can be instructed at the level of 16 BRADSHAW goals and strategies, agents can find ways to "work around" unforeseen problems and exploit new opportunities as they help solve problems. . Task orientation: Agents can be designed to take the context of the person's tasks and situation into account as they present information and take action. . Adaptivity: Agents can use learning algorithms to continually improve their behavior by noticing recurrent patterns of actions and events. Toward Agent-Enabled System Architectures In the future, assistant agents at the user interface and resource-managing agents behind the scenes will increas...

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. This paper reports on first results of a research program that uses partially observable Markov models to robustly track a robot's location in office environments and to direct its goal-oriented actions. The approach explicitly maintains a probability distribution over the possible locations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, and actuator and sensor uncertainty. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the robustness of this approach in controlling an actual indoor mobile robot navigating corridors. 1 Introduction We are interested in the task of long-term autonomous navigation in an office environment (with corridors, foyers, and rooms). While the state of the art in autonomous office nav...

In order to re-use existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known starting point. This paper describes the position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment. Our method is designed to work with standard sensors and is independent of any knowledge about the starting point. It is a Bayesian approach based on certainty grids. In each cell of such a grid we store the probability that this cell refers to the current position of the robot. These probabilities are obtained by integrating the likelihoods of sensor readings over time. Results described in this paper show that our technique is able to reliably estimate the position of a robot in complex environments. Our approach has proven to...

RHINO was the University of Bonn's entry in the 1994 AAAI Robot Competition and Exhibition. RHINO is a mobile robot designed for indoor navigation and manipulation tasks. The general scientific goal of the RHINO project is the development and the analysis of autonomous and complex learning systems. This paper briefly describes the major components of the RHINO control software as they were exhibited at the competition. It also sketches the basic philosophy of the RHINO architecture and discusses some of the lessons that we learned during the competition. I. GENERAL OVERVIEW RHINO, shown in Figure 1, is a B21 mobile robot platform manufactured by Real World Interface Inc. It is equipped with 24 sonar proximity sensors, a dual color camera system mounted on a pan/tilt unit, and two onboard i486 computers. Sonar information is obtained at a rate of 1.3 Hertz, and camera images are processed at a rate of 0.7 Hertz. RHINO communicates with external computers (two SUN Sparcstations) by a tet...

This chapter surveys basic methods for learning maps and high speed autonomous navigation for indoor mobile robots. The methods have been developed in our lab over the past few years, and most of them have been tested thoroughly in various indoor environments. The chapter is targeted towards researchers and engineers who attempt to build reliable mobile robot navigation software.

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We have developed an approach that uses partially observable Markov models to robustly track a robot’s location and integrates it with a planning and execution monitoring approach that uses this information to control the robot’s actions. The approach explicitly maintains a probability distributionover the possiblelocations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, actuator uncertainty, and sensor noise. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the reliability of this approach, especially its ability to smoothly recover from errors in sensing. 1.

Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multi-room environments.

Service robotics is currently a pivotal research area in robotics, with enormous societal potential. Since service robots directly interact with people, nding \natural" and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such asnavigation and manipulation, relatively few robotic systems are equipped with exible user interfaces that permit controlling the robot by \natural " means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through o ce environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures de ned through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to speci c locations that need to be cleaned and instructs the robot to pick up trash. 1.

This paper addresses the problem of determining an object's 3D location from a stream of camera images recorded by mobile robot. The approach presented here allows people to "train" robots to recognize specific objects, by presenting it examples of the object to be recognized. A decision tree method is used to learn significant features of the target object from individual camera images. Individual estimates are integrated over time using Bayes rule, into a probabilistic 3D model of the robot's environment. Experimental results illustrate that the method enables a mobile robot to robustly estimate the 3D location of objects from multiple camera images.