Expecting the Unexpected : Measuring Uncertainties in Mobile Robot Path Planning in Dynamic Envionments

Unexpected obstacles pose significant challenges to mobile robot navigation. In this paper we investigate how, based on the assumption that unexpected obstacles really follow patterns that can be exploited, a mobile robot can learn the locations within an environment that are likely to contain obstacles, and so plan optimal paths by avoiding these locations in subsequent navigation tasks. We propose the DUNC (Dynamically Updating Navigational Confidence) method to do this. We evaluate the performance of the DUNC method by comparing it with existing methods in a large number of randomly generated simulated test environments. our evaluations show that, by learning the likely locations of unexpected obstacles, the DUNC method can plan more efficient paths than existing approaches to this problem

Localization in highly dynamic environments using dual-timescale NDT-MCL

Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual- timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT- NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously pro- posed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.Postprint (author’s final draft

PEIS stol: autonomni robotski stol za kućanstva

There are two main trends in the area of home and service robotics. The classical one aims at the development of a single skilled servant robot, able to perform complex tasks in a passive environment. The second, more recent trend aims at the achievement of complex tasks through the cooperation of a network of simpler robotic devices pervasively embedded in the domestic environment. This paper contributes to the latter trend by describing the PEIS Table, an autonomous robotic table that can be embedded in a smart environment. The robotic table can operate alone, performing simple point-to-point navigation, or it can collaborate with other devices in the environment to perform more complex tasks. Collaboration follows the PEIS Ecology model. The hardware and software design of the PEIS Table are guided by a set of requirements for robotic domestic furniture that differ, to some extent, from the requirements usually considered for service robots.U uslužnoj robotici i robotici za kućanstva postoje dva glavna trenda. Klasičan pristup teži razvoju jednog složenog uslužnog robota koji je sposoban izvršavati složene zadatke u pasivnom okruženju. Dok drugi, nešto noviji pristup, teži rješavanju složenih zadataka kroz suradnju umreženih nešto jednostavnijih robota prožetih kroz cijelo kućanstvo. Ovaj članak svoj doprinos daje drugom pristupu opisujući PEIS stol, autonomni robotski stol koji se može postaviti u inteligentnom okruženju. Robotski stol može djelovati samostalno, navigirajući od točke do točke ili može surađivati s ostalim uređajima u okruženju radi izvršavanja složenijih zadataka. Ta suradnja prati PEIS ekološki model. Dizajn sklopovlja i programske podrške PEIS stola prati zahtjeve za robotsko pokućstvo koji se donekle razlikuju od zahtjeva koji se inače postavljaju za uslužne robote

Development of distributed control architecture for multi-robot systems

The execution of complex tasks by teams of robots has been widely investigated in the last decades, since many operations are too risky or difficult to be performed by humans or by a single robot. The complexity and variety of applications of mobile robotics make the coordination of teams a big problem, as several topologies of control systems, from simple single processes to large networks with distributed elements that are capable of switching function, may be necessary. Although simple solutions exist, more efficient approaches use distributed communication architectures and components abstraction layers. Available proposals provide many components and interfaces, complicating their understanding and operation. This paper presents a generic control architecture that provides the developer with a small amount of elements implemented safely and on high-performance libraries. The simplicity and modularity of the proposal allow implementation of features such as control of heterogeneous robots, data source and command destination transparency and platform and language independence. The ability to support with reliability, transparency and ease the development of various scenarios of autonomous mobile robotics make the proposed architecture a powerful and valuable tool in the design and operation of these systems.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Centro de Tecnologia da Informação Renato Arche

An integrated probabilistic model for scan-matching, moving object detection and motion estimation

Abstract — This paper presents a novel framework for in-tegrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that jointly reasons about scan-matching, moving object detection and their motion estimation is developed. Scan-matching and moving object detection are two important problems for full autonomy of robotic systems in complex dynamic environments. Popular techniques for solving these problems usually address each task in turn disregarding important dependencies. The model developed here jointly reasons about these tasks by performing inference in a probabilistic graphical model. It allows different but related problems to be expressed in a single framework. The experiments demonstrate that jointly reasoning results in better estimates for both tasks compared to solving the tasks individually. I

A new solution to map dynamic indoor environments

Author name used in this publication: G. Q. HuangAuthor name used in this publication: Y. K. Wong2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe