Field test of a navigation system: autonomous cleaning in supermarkets

Endres, H.; Feiten, Wendelin; Lawitzky, Gisbert

doi:10.1109/robot.1998.677424

Cited by 64 publications

(35 citation statements)

References 2 publications

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“…In the last few years, intelligent robots were successfully fielded in hospitals (King, S., and Weiman, C, 1990), museums (Burgard, W. et al, 1999), and office buildings/department stores (Endres, H. et al , 1998), where they perform cleaning services, deliver, educate, or entertain (Schraft, R., and Schmierer, G. , 2005). Robots have also been developed for guiding blind people, as well as robotic aids for the elderly.…”

Section: Introductionmentioning

confidence: 99%

The Development of the Omnidirectional Mobile Home Care Robot

Zou¹

2011

Mobile Robots - Current Trends

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Section: Introductionmentioning

confidence: 99%

The Development of the Omnidirectional Mobile Home Care Robot

Zou¹

2011

Mobile Robots - Current Trends

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“…This paper proposes an efficient probabilistic approach for collaborative multi-robot localization. Our approach is based on Markov localization [53,64,37,9], a family of probabilistic approaches that have recently been applied with great practical success to single-robot localization [7,70,19,29]. In contrast to previous research, which relied on grid-based or coarse-grained topological representations, our approach adopts a sampling-based representation [17,23], which is capable of approximating a wide range of belief functions in real-time.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo Algorithm for Multi-Robot Localization

Fox¹,

Burgard²,

Kruppa³

et al. 1999

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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 paper also describes experimental results obtained using two mobile robots, using computer vision and laser range finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

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“…These robots, for example, are deployed in hospitals [7], museums [4], office buildings [1], and department stores [6], where they perform various services, e.g., deliver, educate, entertain [14] or assist people [13,9]. Additionally, * This work has partly been supported by the EC under contract number IST-2000-29456. a variety of techniques has been developed that allows a robot to estimate the positions of people in its vicinity or to adapt its behavior accordingly.…”

Section: Introductionmentioning

confidence: 99%

Using EM to learn motion behaviors of persons with mobile robots

Bennewitz

Burgard

Thrun³

IEEE/RSJ International Conference on Intelligent Robots and System

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We propose a method for learning models of people's motion behaviors in indoor environments. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser-range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.

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Field test of a navigation system: autonomous cleaning in supermarkets

Cited by 64 publications

References 2 publications

The Development of the Omnidirectional Mobile Home Care Robot

The Development of the Omnidirectional Mobile Home Care Robot

A Monte Carlo Algorithm for Multi-Robot Localization

Using EM to learn motion behaviors of persons with mobile robots

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