RP Lidar Sensor for Multi-Robot Localization using Leader Follower Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 21-32 ◽  
Author(s):  
Bayadir Issa ◽  
Abdulmuttalib Rashid
Keyword(s):  
New Technique ◽  
Robot Localization ◽  
Localization Algorithm ◽  
Unknown Environment ◽  
A New Technique ◽  
Equivalent Distance ◽  
Multi Robot

In this paper, a new technique for multi-robot localization in an unknown environment, called the leader-follower localization algorithm is presented. The framework utilized here is one robot that goes about as a leader and different robots are considered as followers distributed randomly in the environment. Every robot equipped with RP lidar sensors to scan the environment and gather information about every robot. This information utilized by the leader to distinguish and confine every robot in the environment. The issue of not noticeable robots is solved by contrasting their distances with the leader. Moreover, the equivalent distance robot issue is unraveled by utilizing the permutation algorithm. Several simulation scenarios with different positions and orientations are implemented on (3-7) robots to show the performance of the introduced technique.

scholarly journals Distributed Multi-Robot Localization

Robotics ◽  
2013 ◽  
pp. 391-406
Author(s):  
Stefano Panzieri ◽  
Federica Pascucci ◽  
Lorenzo Sciavicco ◽  
Roberto Setola
Keyword(s):  
Information Exchange ◽  
Short Range ◽  
Data Communication ◽  
Robot Localization ◽  
Localization Algorithm ◽  
Dynamic Correction ◽  
Visual Contact ◽  
Full State ◽  
Position Estimate ◽  
Multi Robot

In this chapter, the design of a completely decentralized and distributed multi-robot localization algorithm is presented. The issue is approached using an Interlaced Extended Kalman Filter (IEKF) algorithm. The proposed solution allows the dynamic correction of the position computed by any single robot through information shared during the random rendezvous of robots. The agents are supposed to carry short-range antennas to enable data communication when they have a “visual” contact. The information exchange is limited to the pose variables and the associated covariance matrix. The algorithm combines the robustness of a full-state EKF with the effortlessness of its interlaced implementation. The proposed unsupervised method provides great flexibility by using exteroceptive sensors, even if it does not guarantee the same position estimate accuracy for each agent. However, it can be effective in case of connectivity loss among team robots. Moreover, it does not need synchronization between agents.

Distributed Multi-Robot Localization

2013 ◽  
pp. 1-18
Author(s):  
Stefano Panzieri ◽  
Federica Pascucci ◽  
Lorenzo Sciavicco ◽  
Roberto Setola
Keyword(s):  
Information Exchange ◽  
Short Range ◽  
Data Communication ◽  
Robot Localization ◽  
Localization Algorithm ◽  
Dynamic Correction ◽  
Visual Contact ◽  
Full State ◽  
Position Estimate ◽  
Multi Robot

In this chapter, the design of a completely decentralized and distributed multi-robot localization algorithm is presented. The issue is approached using an Interlaced Extended Kalman Filter (IEKF) algorithm. The proposed solution allows the dynamic correction of the position computed by any single robot through information shared during the random rendezvous of robots. The agents are supposed to carry short-range antennas to enable data communication when they have a “visual” contact. The information exchange is limited to the pose variables and the associated covariance matrix. The algorithm combines the robustness of a full-state EKF with the effortlessness of its interlaced implementation. The proposed unsupervised method provides great flexibility by using exteroceptive sensors, even if it does not guarantee the same position estimate accuracy for each agent. However, it can be effective in case of connectivity loss among team robots. Moreover, it does not need synchronization between agents.

A new technique for the mapping of oxygen tension on the brain surface

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S543-S543
Author(s):  
Satoshi Kimura ◽  
Keigo Matsumoto ◽  
Yoshio Imahori ◽  
Katsuyoshi Mineura ◽  
Toshiyuki Itoh
Keyword(s):  
Oxygen Tension ◽  
New Technique ◽  
Brain Surface ◽  
A New Technique ◽  
The Brain

A new technique to visualize alveolar dynamics in a rabbit model

2009 ◽  
Vol 56 (S 01) ◽  
Author(s):  
J Bickenbach ◽  
R Rossaint ◽  
R Autschbach ◽  
R Dembinski
Keyword(s):  
Rabbit Model ◽  
New Technique ◽  
A New Technique
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