Robot localization in symmetric environment

The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. Monte Carlo Localization as a well known localization algorithm represents a robot\u27s belief by a set of weighted samples. This set of samples approximates the posterior probability of where the robot is located. Our method presents an extension to the MCL algorithm when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The sample sets in MCL often become impoverished when samples are generated in several locations. Our approach incorporates the idea of clustering the samples and organizes them considering to their orientation. Experimental results show our method is able to successfully determine the position of the robot in symmetric environment, while ordinary MCL often fails

Navegação de um robô autónomo semi-industrial usando diferentes técnicas sensoriais

Mestrado em Engenharia Electrotécnica e de ComputadoresEsta dissertação endereça a navegação de um robô móvel semi-industrial usando diferentes técnicas sensoriais. Foi desenvolvido um sistema de Navegação Autónoma com funcionalidades básicas de navegação e integrado um equipamento Laser Range Finder (LRF) para melhorar a navegabilidade num ambiente semi-estruturado interior. A plataforma robótica utilizada é um robô semi-industrial chamado Robuter. A solução desenvolvida mostra como a utilização do Laser Range Finder permite melhorar a navegação em ambientes com corredores e zonas estreitas, onde as manobras são difíceis. Foi implementado um algoritmo para navegação que pode ser adaptado para outros robôs. Os objectivos iniciais foram atingidos e validados experimentalmente.This thesis addresses the navigation of a mobile robot semi-industrial sensory using different techniques. It was developed a system of autonomous navigation with basic features of an integrated navigation and equipment Laser Range Finder (LRF) to improve navigability in a semi-structured interior. The robotics platform used is a robot called semi-industrial Robuter. The solution developed shows such as the use of Laser Range Finder will improve navigation in environments with narrow corridors and areas where the maneuvers are difficult. It implemented an algorithm for navigation that can be adapted to other robots. The initial objectives have been met and validated experimentally

Cooperative Material Handling by Human and Robotic Agents:Module Development and System Synthesis

In this paper we present the results of a collaborative effort to design and implement a system for cooperative material handling by a small team of human and robotic agents in an unstructured indoor environment. Our approach makes fundamental use of human agents\u27 expertise for aspects of task planning, task monitoring, and error recovery. Our system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of human abilities within the present state of the art of autonomous systems. It is designed to allow for and promote cooperative interaction between distributed agents with various capabilities and resources. Our robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. Our robotic agents are not required to be homogeneous with respect to either capabilities or function. Our research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to the basic functioning of such a cooperative multi-agent system. We have constructed a testbed facility for experimenting with distributed multi-agent architectures. The required modular components of this testbed are currently operational and have been tested individually. Our current research focuses on the integration of agents in a scenario for cooperative material handling

An Improved Approach For Multi-Robot Localization

Cooperative multi-robot localization techniques use sensor measurements to estimate poses (locations, orientations) of robots relative to a given map of the environment. Existing approaches update a robot\u27s pose instantly whenever it detects another robot. However, such instant update may not be always necessary and effective, since both robots\u27 pose estimates could be highly uncertain at the time of the detection. In this thesis, we develop a new information exchange mechanism to collaborative multi-robot localization. We also propose a new scheme to calculate how much information is contained in a robot\u27s belief by using entropy. Instead of updating beliefs whenever detection occurs, our approach first compares the beliefs of the robots which are involved in the detection, and then decide whether the information exchange is necessary. Therefore, it avoids unnecessary information exchange whenever one robot perceives another robot. On the other hand, this approach does allow information exchange between detecting robots and such information exchange always contributes positively to the localization process, hence, improving the effectiveness and efficiency of multi-robot localization. The technique has been implemented and tested using two mobile robots as well as simulations. The results indicate significant improvements in localization speed and accuracy when compared to the single mobile robot localization

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed