scholarly journals Development of the navigational system in homing pigeons: increase in complexity of the navigational map

2013 ◽  
Vol 216 (14) ◽  
pp. 2675-2681 ◽  
Author(s):  
I. Schiffner ◽  
R. Wiltschko
Keyword(s):  
Homing Pigeons ◽  
Navigational Map ◽  
Navigational System

Development of the navigational map in homing pigeons: effects of flight experience on orientation performance

2003 ◽  
Vol 66 (6) ◽  
pp. 1093-1099 ◽  
Author(s):  
Francesca Odetti ◽  
Paolo Ioalè ◽  
Anna Gagliardo
Keyword(s):  
Homing Pigeons ◽  
Flight Experience ◽  
Navigational Map

043.18
DIFFERENTIAL ROLE OF LEFT and RIGHT HIPPOCAMPAL FORMATION IN THE ACQUISITION OF THE NAVIGATIONAL MAP IN HOMING PIGEONS

2000 ◽  
Vol 12 (9) ◽  
pp. 3451-3451
Author(s):  
A. Gagliardo ◽  
P. Ioalè ◽  
F. Odetti ◽  
V. P. Bingman ◽  
G. Vallortigara
Keyword(s):  
Hippocampal Formation ◽  
Homing Pigeons ◽  
Navigational Map ◽  
Left And Right

Hippocampal lesions do not disrupt navigational map retention in homing pigeons under conditions when map acquisition is hippocampal dependent

2004 ◽  
Vol 153 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Anna Gagliardo ◽  
Paolo Ioalè ◽  
Francesca Odetti ◽  
Meghan C Kahn ◽  
Verner P Bingman
Keyword(s):  
Homing Pigeons ◽  
Navigational Map ◽  
Hippocampal Lesions

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

Design of quantum sensor to duplicate European Robins navigational system

2021 ◽  
Vol 322 ◽  
pp. 112636
Author(s):  
Ahmad Salmanogli ◽  
Dincer Gokcen
Keyword(s):  
Navigational System
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