An exploration and navigation approach for indoor mobile robots considering sensor's perceptual limitations

Romero, Leonardo; Morales, Eduardo F.; Sucar, L. Enrique

doi:10.1109/robot.2001.933092

Cited by 22 publications

(14 citation statements)

References 6 publications

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“…On the other hand, the odometer can be used to calculate the moving distance [4]. Some researches on indoor navigation have assumed that the robot wheels will never slide and no error exists.…”

Section: Introductionmentioning

confidence: 99%

Study of an Innovative Indoor Robotic Navigation Approach Based on Beacons and PSD

Wang

2016

MATEC Web of Conferences

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Abstract. In this paper, innovative indoor navigation methods have been proposed to meet the challenges in robotic navigation systems. The general positioning methods for robotic navigation include vision-based approaches, WIFI beacons, infrared beacons, ultrasonic beacons, etc. However, the common problem with these methods is their inaccuracy. Especially, improving the precision of robotic positioning mechanisms is the key to indoor navigation systems. This paper proposes an approach that combines the external rotating beacon with an internal rotation of position sensitive devices (PSD) which are installed on the robot. While two infrared beams from an external beacon source are equally projected to both sides of the PSD, the robot's position can be calculated precisely. The high performance and accurate results can be achieved by optimizing the rotation aligning time, dividing the working area, and compensating errors with information fusion. In comparison with other generic approaches, this proposed innovative approach requires less computing resources and is easier to implement due to its much lower complexity for the computing algorithms.

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Section: Introductionmentioning

confidence: 99%

Study of an Innovative Indoor Robotic Navigation Approach Based on Beacons and PSD

Wang

2016

MATEC Web of Conferences

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“…This has led to work on robots like Minerva [1] that used a "coastal planner" to avoid navigation paths with poor information content perceivable to the robot. Exploration and navigation approaches that account for perceptual limitations of robots [2] have been studied as well. These approaches acknowledge the limitations in perception, and seek to avoid areas where perception is poor.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-sensor Mobile Robot Localization for Diverse Environments

Biswas

Veloso

2014

RoboCup 2013: Robot World Cup XVII

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Abstract. Mobile robot localization with different sensors and algorithms is a widely studied problem, and there have been many approaches proposed, with considerable degrees of success. However, every sensor and algorithm has limitations, due to which we believe no single localization algorithm can be "perfect," or universally applicable to all situations. Laser rangefinders are commonly used for localization, and state-of-theart algorithms are capable of achieving sub-centimeter accuracy in environments with features observable by laser rangefinders. Unfortunately, in large scale environments, there are bound to be areas devoid of features visible by a laser rangefinder, like open atria or corridors with glass walls. In such situations, the error in localization estimates using laser rangefinders could grow in an unbounded manner. Localization algorithms that use depth cameras, like the Microsoft Kinect sensor, have similar characteristics. WiFi signal strength based algorithms, on the other hand, are applicable anywhere there is dense WiFi coverage, and have bounded errors. Although the minimum error of WiFi based localization may be greater than that of laser rangefinder or depth camera based localization, the maximum error of WiFi based localization is bounded and less than that of the other algorithms. Hence, in our work, we analyze the strengths of localization using all three sensors -using a laser rangefinder, a depth camera, and using WiFi. We identify sensors that are most accurate at localization for different locations on the map. The mobile robot could then, for example, rely on WiFi localization more in open areas or areas with glass walls, and laser rangefinder and depth camera based localization in corridor and office environments.

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“…This approach is an improved version of the global localization approach given in [10]. We use a Markov localization (see [7,10]) in both phases, to represent and update the set H of hypotheses, and predict movements of the robot in order to eliminate hypotheses. The main contribution of this paper is to predict movements using a Markov localization that includes an uncertainty model for the movements of the robot.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this paper is to predict movements using a Markov localization that includes an uncertainty model for the movements of the robot. Previous approaches [8,5,10] only consider a determinist or ideal model for the robot (a robot with a perfect odometer) during the prediction process.…”

Section: Introductionmentioning

confidence: 99%

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Solving the Global Localization Problem for Indoor Mobile Robots

Romero

Morales

Sucar

2003

Lecture Notes in Computer Science

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Abstract. Global localization is the problem of determining the position of a robot under global uncertainty. This problem can be divided in two phases: 1) from the sensor data (or sensor view), determine clusters of hypotheses where the robot can be; and 2) devise a strategy by which the robot can correctly eliminate all but the right location. In the second phase, previous approaches consider an ideal robot, a robot with a perfect odometer, to predict robot movements. This paper introduces a non deterministic prediction approach based on a Markov localization that include an uncertainty model for the movements of the robot. The non deterministic model can help to solve situations where a deterministic or ideal model fails. Hypotheses are clustered and a greedy search algorithm determines the robot movements to reduce the number of clusters of hypotheses. This approach is tested using a simulated mobile robot with promising results.

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An exploration and navigation approach for indoor mobile robots considering sensor's perceptual limitations

Cited by 22 publications

References 6 publications

Study of an Innovative Indoor Robotic Navigation Approach Based on Beacons and PSD

Study of an Innovative Indoor Robotic Navigation Approach Based on Beacons and PSD

Multi-sensor Mobile Robot Localization for Diverse Environments

Solving the Global Localization Problem for Indoor Mobile Robots

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