Guaranteed Mobile Robot Tracking Using Robust Interval Constraint Propagation

Langerwisch, Marco; Wagner, Bernd

doi:10.1007/978-3-642-33515-0_36

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…It is used for robust bounded-error estimation [6], for localization [21], for probabilistic estimation [18] or for certified calibration of robots [9]. Theorem 1.…”

Section: Pavermentioning

confidence: 99%

Introduction to the algebra of separators with application to path planning

Jaulin

Desrochers

2014

Engineering Applications of Artificial Intelligence

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Contractor algebra is a numerical tool based on interval analysis which makes it possible to solve many nonlinear problems arising in robotics, such as identification, path planning or robust control. This paper presents the new notion of separators which is a pair of complementary contractors and presents the corresponding algebra. Using the separator algebra inside a paver will allow us to get an inner and an outer approximation of the solution set in a much simpler way than using any other interval approach. A path planning problem will then be considered in order to illustrate the principle of the approach.

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“…It is used for robust bounded-error estimation [6], for localization [21], for probabilistic estimation [18] or for certified calibration of robots [9]. Theorem 1.…”

Section: Pavermentioning

confidence: 99%

Introduction to the algebra of separators with application to path planning

Jaulin

Desrochers

2014

Engineering Applications of Artificial Intelligence

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“…All robots in the scheme have a directional sensor. Langerwisch and Wanger [10] proposed a system which tracks a mobile robot in a feature-based map using a 2D laser rangefinder and wheel odometry. Ramya et al [3,11] proposed a cooperative tracking method which each sensor node stores neighbor node identifier, intersection points of sensing circle of the node.…”

Section: Related Workmentioning

confidence: 99%

Tracking Mobile Robot in Indoor Wireless Sensor Networks

Zhang

Lim

Karimi

2014

Mathematical Problems in Engineering

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This work addresses the problem of tracking mobile robots in indoor wireless sensor networks (WSNs). Our approach is based on a localization scheme with RSSI (received signal strength indication) which is used widely in WSN. The developed tracking system is designed for continuous estimation of the robot’s trajectory. A WSN, which is composed of many very simple and cheap wireless sensor nodes, is deployed at a specific region of interest. The wireless sensor nodes collect RSSI information sent by mobile robots. A range-based data fusion scheme is used to estimate the robot’s trajectory. Moreover, a Kalman filter is designed to improve tracking accuracy. Experiments are provided to assess the performance of the proposed scheme.

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“…Based on such technique, Langerwisch and Wagner present a method for localization using laser sensors, which is able to detect and mark outliers in the laser scans [20]. Another method of localization using range sensors is the one from Guyonneau et al [21].…”

Section: Related Workmentioning

confidence: 99%

“…Then, all particles are tested against the interval results. Particles that fall outside the search space are discarded, and to each discarded particle, a new particle is randomly created inside the current search space (lines [16][17][18][19][20][21]. The particles are weighted (line 22), resampled (line 23), and the weighted average of the particles is selected to represent the robot pose (line 24).…”

Section: Improving the Precision Of Localization With A Set-inversionmentioning

confidence: 99%

Improving the Precision of AUVs Localization in a Hybrid Interval-Probabilistic Approach Using a Set-Inversion Strategy

et al. 2014

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One of the fundamental tasks of robotics is to solve the localization problem, in which a robot must determine its true pose without any knowledge on its initial location. In underwater environments, this is specially hard due to sensors restrictions. For instance, many times, the localization process must rely on information from acoustic sensors, such as transponders. We propose a method to deal with this scenario, that consists in a hybridization of probabilistic and interval approaches, aiming to overcome the weaknesses found in each approach and improve the precision of results. In this paper, we use the set inversion via interval analysis (SIVIA) technique to reduce the region of uncertainty about robot localization, and a particle filter to refine the estimates. With the information provided by SIVIA, the distribution of particles can be concentrated in regions of higher interest. We compare this approach with a previous hybrid approach using contractors instead of SIVIA. Experiments with simulated data show that our hybrid method using SIVIA provides more accurate results than the method using contractors.

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Guaranteed Mobile Robot Tracking Using Robust Interval Constraint Propagation

Cited by 8 publications

References 8 publications

Introduction to the algebra of separators with application to path planning

Introduction to the algebra of separators with application to path planning

Tracking Mobile Robot in Indoor Wireless Sensor Networks

Improving the Precision of AUVs Localization in a Hybrid Interval-Probabilistic Approach Using a Set-Inversion Strategy

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