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.


Sensor Models. (a) Orientation; (b) Ultrasonic belt.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-02487: Sensor Models. (a) Orientation; (b) Ultrasonic belt.

Mentions: The robot is equipped with an electronic compass that provides information (z1,t+1) of the robot orientation . However, due to the discontinuity and periodicity of the angular data, the measurement function h1(·) should receive a special treatment, which takes into account if the angular discontinuity forms part of the shortest or longest path that exist, according to Figure 3(a), between the compass measurement z1,t+1 and the robot orientation . When that happens, and in order to make the robot location comparable to the compass measurement, the value should be incremented/decremented for 2π radians. The measurement function h1(·) that represents this behavior is presented in Equation (5). The first expression is for the case where the discontinuity is in the shortest path and z1,t+1 is v1 and is v2. The second expression is for the case where the discontinuity is in the shortest path and z1,t+1 is v2 and is v1. And the last expression is for the case where the discontinuity is not in the shortest path.(5)z1,t+1={pt+1θ−2πif z1,t+1∈[0,π) ∧/z1,t+1−pt+1θ/ >πpt+1θ+2πif z1,t+1∈[π,2π) ∧/z1,t+1−pt+1θ/ >πpt+1θotherwise


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)

Sensor Models. (a) Orientation; (b) Ultrasonic belt.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-02487: Sensor Models. (a) Orientation; (b) Ultrasonic belt.
Mentions: The robot is equipped with an electronic compass that provides information (z1,t+1) of the robot orientation . However, due to the discontinuity and periodicity of the angular data, the measurement function h1(·) should receive a special treatment, which takes into account if the angular discontinuity forms part of the shortest or longest path that exist, according to Figure 3(a), between the compass measurement z1,t+1 and the robot orientation . When that happens, and in order to make the robot location comparable to the compass measurement, the value should be incremented/decremented for 2π radians. The measurement function h1(·) that represents this behavior is presented in Equation (5). The first expression is for the case where the discontinuity is in the shortest path and z1,t+1 is v1 and is v2. The second expression is for the case where the discontinuity is in the shortest path and z1,t+1 is v2 and is v1. And the last expression is for the case where the discontinuity is not in the shortest path.(5)z1,t+1={pt+1θ−2πif z1,t+1∈[0,π) ∧/z1,t+1−pt+1θ/ >πpt+1θ+2πif z1,t+1∈[π,2π) ∧/z1,t+1−pt+1θ/ >πpt+1θotherwise

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.