scholarly journals An Algorithmic Approach to Adapting Edge-based Devices for Autonomous Robotic Navigation

Author(s):  
Mbadiwe Benyeogor ◽  
Kosisochukwu Nnoli ◽  
Oladayo Olakanmi ◽  
Olusegun Lawal ◽  
Eric Gratton ◽  
...  
Author(s):  
J. Parker Mitchell ◽  
Grant Bruer ◽  
Mark E. Dean ◽  
James S. Plank ◽  
Garrett S. Rose ◽  
...  

Author(s):  
Angel Sanchez Garcia ◽  
Homero Rios Figueroa ◽  
Antonio Marin Hernandez ◽  
Ericka Rechy Ramirez ◽  
David Oliva Uribe

2021 ◽  
Vol 54 (2) ◽  
pp. 259-264
Author(s):  
J.W. Kok ◽  
E. Torta ◽  
M.A. Reniers ◽  
J.M. van de Mortel-Fronczak ◽  
M.J.G. van de Molengraft

Author(s):  
Chen Ning ◽  
Li Menglu ◽  
Yuan Hao ◽  
Su Xueping ◽  
Li Yunhong

Abstract Pedestrian detection is widely applied in surveillance, autonomous robotic navigation, and automotive safety. However, there are many occlusion problems in real life. This paper summarizes the research progress of pedestrian detection technology with occlusion. First, according to different occlusion, it can be divided into two categories: inter-class occlusion and intra-class occlusion. Second, it summarizes the traditional method and deep learning method to deal with occlusion. Furthermore, the main ideas and core problems of each method model are analyzed and discussed. Finally, the paper gives an outlook on the problems to be solved in the future development of pedestrian detection technology with occlusion.


Author(s):  
Reza Rahmadian ◽  
Mahendra Widyartono

Interest on robotic agriculture system has led to the development of agricultural robots that helps to improve the farming operation and increase the agriculture productivity. Much research has been conducted to increase the capability of the robot to assist agricultural operation, which leads to development of autonomous robot. This development provides a means of reducing agriculture’s dependency on operators, workers, also reducing the inaccuracy caused by human errors. There are two important development components for autonomous navigation. The first component is Machine vision for guiding through the crops and the second component is GPS technology to guide the robot through the agricultural fields.


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