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Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements.

Besada-Portas E, Lopez-Orozco JA, Lanillos P, de la Cruz JM - Sensors (Basel) (2012)

Bottom Line: This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems.The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors.Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches.

View Article: PubMed Central - PubMed

Affiliation: Departamento Arquitectura de Computadores y Automatica, Universidad Complutense de Madrid, Madrid, Spain. evabes@dacya.ucm.es

ABSTRACT
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.

No MeSH data available.


Out-Of-Sequence Problem. (a) Non-delayed data; (b) 1-step lag delay data; (c) N-step lag delay data.
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f1-sensors-12-02487: Out-Of-Sequence Problem. (a) Non-delayed data; (b) 1-step lag delay data; (c) N-step lag delay data.

Mentions: In order to achieve both objectives, the location module can implement any of the sequential estimators that can deal with the uncertainty and characteristics associated to the robot dynamics and sensors, such as the Kalman, Information or Particle Filter (KF, IF, PF [1–4]). When we apply their basic formulations to the robot estimation problem, they sequentially estimate the current time robot location based on the current time measurements and previous time location estimate. Therefore, when the localization module implements the basic formulation of these filters (whose behavior is schematized in Figure 1(a), representing the dependency of the location estimate on the current time measurements with the arrows and on the previous time location estimate with the arcs), the location module requires to have all the measurements associated to the current time step before obtaining the estimate related to it.


Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements.

Besada-Portas E, Lopez-Orozco JA, Lanillos P, de la Cruz JM - Sensors (Basel) (2012)

Out-Of-Sequence Problem. (a) Non-delayed data; (b) 1-step lag delay data; (c) N-step lag delay data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376572&req=5

f1-sensors-12-02487: Out-Of-Sequence Problem. (a) Non-delayed data; (b) 1-step lag delay data; (c) N-step lag delay data.
Mentions: In order to achieve both objectives, the location module can implement any of the sequential estimators that can deal with the uncertainty and characteristics associated to the robot dynamics and sensors, such as the Kalman, Information or Particle Filter (KF, IF, PF [1–4]). When we apply their basic formulations to the robot estimation problem, they sequentially estimate the current time robot location based on the current time measurements and previous time location estimate. Therefore, when the localization module implements the basic formulation of these filters (whose behavior is schematized in Figure 1(a), representing the dependency of the location estimate on the current time measurements with the arrows and on the previous time location estimate with the arcs), the location module requires to have all the measurements associated to the current time step before obtaining the estimate related to it.

Bottom Line: This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems.The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors.Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches.

View Article: PubMed Central - PubMed

Affiliation: Departamento Arquitectura de Computadores y Automatica, Universidad Complutense de Madrid, Madrid, Spain. evabes@dacya.ucm.es

ABSTRACT
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.

No MeSH data available.