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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.


Robot. (a) Schema; (b) Frontal View; (c) Lateral View.
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f2-sensors-12-02487: Robot. (a) Schema; (b) Frontal View; (c) Lateral View.

Mentions: The robot, represented in Figure 2, is equipped with two motorized wheels (independently controlled by two DC drives and placed, separated at b distance, under the lower robot platform) and two castor wheels (placed in the front and back of the same platform). The dynamic behavior of a robot with this arrangement, which lets the robot rotate around its Z-axis with an angular speed dependent on the control speed applied to each wheel, is going to be modeled as a non-linear system. The sensorial devices of the robot used to estimate the robot location are: two encoders attached to the motorized wheels that provide information about the displacement of the wheels, a magnetic compass that provides information of the robot orientation, and an ultrasonic belt that provides information of the robot distance to known landmarks. These three types of sensors are going to be modeled with linear and non-linear models. Finally, it is worth noting that the robot is also equipped with an stereoscopic visual system and an infrared belt, whose information is only used in the module in charge of updating the map information.


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)

Robot. (a) Schema; (b) Frontal View; (c) Lateral View.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-12-02487: Robot. (a) Schema; (b) Frontal View; (c) Lateral View.
Mentions: The robot, represented in Figure 2, is equipped with two motorized wheels (independently controlled by two DC drives and placed, separated at b distance, under the lower robot platform) and two castor wheels (placed in the front and back of the same platform). The dynamic behavior of a robot with this arrangement, which lets the robot rotate around its Z-axis with an angular speed dependent on the control speed applied to each wheel, is going to be modeled as a non-linear system. The sensorial devices of the robot used to estimate the robot location are: two encoders attached to the motorized wheels that provide information about the displacement of the wheels, a magnetic compass that provides information of the robot orientation, and an ultrasonic belt that provides information of the robot distance to known landmarks. These three types of sensors are going to be modeled with linear and non-linear models. Finally, it is worth noting that the robot is also equipped with an stereoscopic visual system and an infrared belt, whose information is only used in the module in charge of updating the map information.

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.