Limits...
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.


Real Experiments. (a) Sensor behavior; (b) Sensor behavior; (c) Sensor behavior; (d) State; (d) Covariances.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3376572&req=5

f7-sensors-12-02487: Real Experiments. (a) Sensor behavior; (b) Sensor behavior; (c) Sensor behavior; (d) State; (d) Covariances.

Mentions: The results of the experiment are presented in the sensorial behavior, state and covariance graphics of Figure 7. The sensorial behavior graphics (Figure 7(a), 7(b) and 7(c) show that all the measurements, except those provided by the encoders, arrive delayed and out of sequence at the location module. Besides, although the compass and encoder measurements are usually valid, the sonar ones are usually rejected by EIFAsyn, because they are either (1) associated to the unknown objects of the map and can not be used by EIFAsyn or because (2) they are rejected by the EIFAsyn validation test as their error, originated for instance from bounces of the sonar signals in the multiple walls of the bottom U-shape of the hall, is not included in the models. Within the unknown object group fall all the sonar measurements outside the cyan squares in the sensorial behavior graphics, i.e., all the measurements by sonar S5 and S4 (due to the round object), all by S8 (due to the square object), the two first by S7 (due to the square and round object respectively), the first group by S3 (due to the square object), the one at t=60 s by S9 (due to the square object), and the ones by S6 up to t=80 s (due the first group to the square and the second to the round one).


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)

Real Experiments. (a) Sensor behavior; (b) Sensor behavior; (c) Sensor behavior; (d) State; (d) Covariances.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-02487: Real Experiments. (a) Sensor behavior; (b) Sensor behavior; (c) Sensor behavior; (d) State; (d) Covariances.
Mentions: The results of the experiment are presented in the sensorial behavior, state and covariance graphics of Figure 7. The sensorial behavior graphics (Figure 7(a), 7(b) and 7(c) show that all the measurements, except those provided by the encoders, arrive delayed and out of sequence at the location module. Besides, although the compass and encoder measurements are usually valid, the sonar ones are usually rejected by EIFAsyn, because they are either (1) associated to the unknown objects of the map and can not be used by EIFAsyn or because (2) they are rejected by the EIFAsyn validation test as their error, originated for instance from bounces of the sonar signals in the multiple walls of the bottom U-shape of the hall, is not included in the models. Within the unknown object group fall all the sonar measurements outside the cyan squares in the sensorial behavior graphics, i.e., all the measurements by sonar S5 and S4 (due to the round object), all by S8 (due to the square object), the two first by S7 (due to the square and round object respectively), the first group by S3 (due to the square object), the one at t=60 s by S9 (due to the square object), and the ones by S6 up to t=80 s (due the first group to the square and the second to the round one).

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.