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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.


Experiments Setup. (a) Map Objects; (b) Simulated Experiment; (c) Real Experiment.
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f5-sensors-12-02487: Experiments Setup. (a) Map Objects; (b) Simulated Experiment; (c) Real Experiment.

Mentions: Figure 5(a) shows the setup of both experiments. The red dots represent all the known corners while the blue lines represent all the known walls. In the simulated experiment, the robot is placed in the initial position (o), oriented towards the final position (*), and controlled by applying equal speeds to its motorized wheels in order to move it around the angular indetermination [0, 2π]. Therefore, it follows the blue trajectory presented in Figure 5(b). In the real experiment, the robot is placed in the initial position (o) and required to go to the final position (*), unknowing that there are the square and round objects (marked in green in Figure 5(a)) in the hall. The control software of the robot where the OOS algorithm is embedded [52] makes the robot initially go towards the final position until it locates the unknown objects, updates the occupancy grid [53], and replans new trajectories to avoid the unknown objects locations. Therefore, the robot follows the red trajectory presented in Figure 5(c).


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)

Experiments Setup. (a) Map Objects; (b) Simulated Experiment; (c) Real Experiment.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-02487: Experiments Setup. (a) Map Objects; (b) Simulated Experiment; (c) Real Experiment.
Mentions: Figure 5(a) shows the setup of both experiments. The red dots represent all the known corners while the blue lines represent all the known walls. In the simulated experiment, the robot is placed in the initial position (o), oriented towards the final position (*), and controlled by applying equal speeds to its motorized wheels in order to move it around the angular indetermination [0, 2π]. Therefore, it follows the blue trajectory presented in Figure 5(b). In the real experiment, the robot is placed in the initial position (o) and required to go to the final position (*), unknowing that there are the square and round objects (marked in green in Figure 5(a)) in the hall. The control software of the robot where the OOS algorithm is embedded [52] makes the robot initially go towards the final position until it locates the unknown objects, updates the occupancy grid [53], and replans new trajectories to avoid the unknown objects locations. Therefore, the robot follows the red trajectory presented in Figure 5(c).

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.