scholarly journals Development of Vision Based Person Following Module for Mobile Robots in RT-Middleware

10.5772/8831 ◽  
2010 ◽  
Author(s):  
Hiroshi Takemura ◽  
Zentaro Nemoto ◽  
Keita Ito ◽  
Hiroshi Mizoguchi
2009 ◽  
Vol 06 (03) ◽  
pp. 147-157
Author(s):  
HEMIN OMER LATIF ◽  
NASSER SHERKAT ◽  
AHMAD LOTFI

In the effort of developing natural means for human-robot interaction (HRI) significant amount of research has been focusing on Person-Following (PF) for mobile robots. PF, which generally consists of detecting, recognizing and following people, is believed to be one of the required functionalities for most future robots that share their environments with their human companions. Research in this field is mostly directed towards fully automating this functionality, which makes the challenge even more tedious. Focusing on this challenge leads research to divert from other challenges that coexist in any PF system. A natural PF functionality consists of a number of tasks that are required to be implemented in the system. However, in more realistic life scenarios, not all the tasks required for PF need to be automated. Instead, some of these tasks can be operated by human operators and therefore require natural means of interaction and information acquisition. In order to highlight all the tasks that are believed to exist in any PF system, this paper introduces a novel taxonomy for PF. Also, in order to provide a natural means for HRI, TeleGaze is used for information acquisition in the implementation of the taxonomy. TeleGaze was previously developed by the authors as a means of natural HRI for teleoperation through eye-gaze tracking. Using TeleGaze in the aid of developing PF systems is believed to show the feasibility of achieving a realistic information acquisition in a natural way.


Author(s):  
Wilma Pairo ◽  
Javier Ruiz-del-Solar ◽  
Rodrigo Verschae ◽  
Mauricio Correa ◽  
Patricio Loncomilla

2021 ◽  
Vol 11 (9) ◽  
pp. 4165
Author(s):  
Redhwan Algabri ◽  
Mun-Taek Choi

The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment.


2020 ◽  
Vol 14 (2) ◽  
pp. 2965-2968 ◽  
Author(s):  
Lei Pang ◽  
Zhiqiang Cao ◽  
Junzhi Yu ◽  
Peiyu Guan ◽  
Xuechao Chen ◽  
...  

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