Abstract: – AoA(Angle of Arrival) is a well known method used for positioning in providing services such as E911, and for other military and civil radio-location applications, such as sonars and radars. Although devices such as GPS receivers and digital compasses provide good positioning and orientation outdoors, there are many applications requiring the same facilities indoors, where line of sight access to satellites is unavailable, or earth magnetic readings are unreliable. We propose a method for all nodes to determine their orientation and position in an ad hoc network where only a fraction of nodes have the positioning capabilities, under the assumption that each node has the AoA capability. Keywords: – ad hoc networks, positioning, orientation, digital compass, AoA 1

This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence. At its heart, the software approach relies on probabilistic computation, on-line learning, and any-time algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot's operation. Special emphasis is placed on the design of interactive capabilities that appeal to people's intuition. The interface provides new means for human-robot interaction with crowds of people in public places, and it also provides people all around the world with the ability to establish a "virtual telepresence" using the Web. To illustrate our approach, results are reported obtained in mid-...

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.

This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva's software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes

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 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.

This paper presents a new sensing modality for multirobot exploration. The approach is based on using a pair of robots that observe each other, and act in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. The proposed approach improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reectance properties. Two dierent algorithms, based on the size of the environment, are introduced, with a complexity analysis, and experimental results in simulation and with real robots. Keywords: Exploration, Mapping, Multiple Robots, Cooperative Localization. 1. Introduction In this paper we discuss the benets of cooperative localization during the exploration of a large environment. A new

Localization is a critical issue in mobile robotics. If the robot does not know where it is, it, cannot effectively plan movements, locate objects, or reach goals. In this paper, we describe probabilistic self-localization techniques for mobile robots that are based on the principal of maximum-likelihood estimation. The basic method is to compare a map generated at the current robot position to a previously generated map of the environment to prohabilistically maximize the agreement between the maps. This method is able to operate in both indoor and outdoor environments using either discrete features or an occupancy grid to represent the world map. The map may be generated using any method to detect features in the robot's surroundings, including vision, sonar, a d laser range-finder. A global search of the pose space is performed that guarantees that the best position in a discretized pose space is found according to the probabilistic: map agreement measure. In addition, fitting the likelihood function with a parameterized smface allows both subpixel localization and uncertainty estimation to be performed. The application of these techniques in several experiments is described, including experimental localization results for the Sojourner Mars rover. 1

We consider the problem of localizing a robot in a known environment modeled by a simple polygon P . We assume that the robot has a map of P but is placed at an unknown location inside P . From its initial location, the robot sees a set of points called the visibility polygon V of its location. In general, sensing at a single point will not suffice to uniquely localize the robot, since the set H of points in P with visibility polygon V may have more than one element. Hence, the robot must move around and use range sensing and a compass to determine its position (i.e.

A fast simulated annealing algorithm is developed for automatic object recognition. The object recognition problem is addressed as the problem of best describing a match between a hypothesized object and an image. The normalized correlation coeficient is used as a measure of the match. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, e.g., trafic signs, can be recognized by a navigating robot. We illustrate the performance of our algorithm with real-world images of complicated scenes with traffic signs. False positive matches occur only for templates with very small information content. To avoid false positive matches, we propose a method to select model images for robust object recognition by measuring the information content of the model images. The algorithm works well in noisy images for model images with high information content.