Catálogo de publicaciones - libros

In this paper, we investigate the use of high resolution aerial LADAR (LAser Detection And Ranging) data for autonomous mobile robot navigation in natural environments. The use of prior maps from aerial LADAR survey is considered for enhancing system performance in two areas. First, the prior maps are used for registration with the data from the robot in order to compute accurate localization in the map. Second, the prior maps are used for computing detailed traversability maps that are used for planning over long distances. Our objective is to assess the key issues in using such data and to report on a first batch of experiments in combining high-resolution aerial data and on-board sensing.

The mobile robot which traverses on a rough terrain, should have an ability to recognize the shape of the ground surface and to examine the traversability by itself. Even if the robot is remotely controlled by operator, such an ability is still very important to save the operator’s load and to keep safety. For this purpose, the robot measures the front ground, and generate the local elevation map which represents the area where the robot is going to pass, and examines that the wheels can pass through and the bottom surface of robot body dose not contact with ground. This paper reports a simple method of traversability test for the wheeled mobile robot and show an experimental system with some results.

This is a technical report on an outdoor navigation for autonomous mobile robot based on DGPS and odometry positioning data. The robot position is estimated by fusion of DGPS and odometry. However, DGPS measurement data suffer from multi-path or other effects near high building and high trees. Thus, it is necessary to pick up only reliable and accurate DGPS measurement data when the robot position is corrected by data fusion of DGPS and odometry. In this paper, the authors propose a selection method of erroneous DGPS measurement data and a rule of data fusion for robot position correction. Finally, the authors equip a mobile robot with the proposed correction method and the robot autonomously navigates in a campus walkway.

The presented contribution describes an approach to preprocessing and fusion of additional vehicle onboard sensors – the odometer and accelerometer, all targeted to serve as optional and temporary substitute for GPS-like navigation. The suggested solution explores a rule-based system for mutual substitutions and calibrations of the used sensors depending on actual conditions. The only usage of the GPS here stands in providing regular position calibrations and serves as a reference method for evaluation of the presented results. The presented solutions have been experimentally tested with real-world data as shown in the experimental part of the paper.

data fusion, vehicle locator, odometer, accelerometer, odometry corrections, inertial navigation, satellite navigation

This paper studies the use of wireless Ethernet (Wi-Fi) as a localization sensor for mobile robots. Wi-Fi-based localization relies on the existence of one or more Wi-Fi devices in the environment to act as beacons, and uses signal strength information from those beacons to localize the robot. Through the experiments described in this paper, we explore the general properties of Wi-Fi in indoor environments, and assess both the accuracy and utility of Wi-Fi-based localization.

In this paper, the use of medial axis in mobile robot localization is extended. We describe the extraction of robust medial axis from a local occupancy grid to form a global map of the environment. We do not require any special geometric primitives to be present, and we allow substantial changes in the appearance of the environment, as long as the overall structure stays intact. We then describe how to use such a map with Monte Carlo Localization. Two approaches are treated. The first one relies on extracting salient features (tripods) from the local grid. The second one makes use of salient points in the global map and works well, even if no useful medial axis can be extracted during localization due to many new objects in formerly free space. This work aims to develop an efficient navigation system that requires less assumptions about the environment and its dynamics than current systems do.

In this paper, we introduce a novel approach to multirobot coordination that works by simultaneously distributing task allocation, mission planning, and execution among members of a robot team. By combining traditional hierarchical task decomposition techniques with recent developments in market-based multirobot control, we obtain an efficient and robust distributed system capable of solving complex problems. Essentially, we have extended the TraderBots market-based architecture to include a mechanism that distributes tasks among robots at multiple levels of abstractions, represented as task trees. Results are presented for a simulated area reconnaissance scenario.

This paper addresses the problem of simultaneous localization and mapping (SLAM) for teams of collaborating vehicles where the communication bandwidth is limited. We present a novel SLAM algorithm that enables multiple vehicles to acquire a joint map, but which can cope with arbitrary latency and bandwidth limitations such as typically found in airborne vehicle applications. The key idea is to represent maps in information form (negative log-likelihood), and to selectively communicate subsets of the information tailored to the available communication resources. We show that our communication scheme preserves the consistency, which has important ramifications for data association problems. We also provide experimental results that illustrate the effectiveness of our approach in comparison with previous techniques.

Inherent dangers in mining operations motivate the use of robotic technology for addressing hazardous situations that prevent human access. In the context of this case study, we examine the application of a robotic tool for map verification and void profiling in abandoned limestone mines for analysis of cavity extent. To achieve this end, our device enables remote, highly accurate measurements of the subterranean voids to be acquired. In this paper we discuss the design of the robotic tool, demonstrate its application in void assessment for prevention and response to subsidence, and present results from a case study performed in the limestone mines of Kansas City, Kansas.

A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. In particular, target tracking is still challenging in urban traffic situations, because of the large number of rapidly maneuvering targets. The goal of this paper is to present an original way to perform target position and velocity estimation, based on the occupancy grid framework. The main interest of this method is to avoid the decision problem of classical multitarget tracking algorithms. Obtained occupancy grids are combined with danger estimation to perform an elementary task of obstacle avoidance with an electric car.