Development of an Environment Recognition System for Multiple Mobile Robots Controlled by a Human

2001 ◽  
Vol 34 (19) ◽  
pp. 321-326
Author(s):  
Akio Nakamura ◽  
Jun Ota ◽  
Shinichiro Kaneko ◽  
Takashi Matsumoto ◽  
Tamio Arai
1997 ◽  
Vol 9 (5) ◽  
pp. 380-386
Author(s):  
Toshiyuki Kumaki ◽  
◽  
Masahito Nakajima ◽  
Masayoshi Kakikura ◽  

This article, concerned with a part of the research on distributed coordination work by multiple robots, discusses an algorithm for creating maps of unknown environments which are searched for and observed by multiple mobile robots, and on the results of a simulation experiment using this algorithm. This algorithm comprises a moving method, an observation method, and a task planning method which are intended to help the multiple mobile robots carry out an efficient search of unknown environments.


2016 ◽  
Vol 817 ◽  
pp. 122-129 ◽  
Author(s):  
Aleksander Budziński ◽  
Tomasz Buratowski ◽  
Mariusz Giergiel

The article describes the concept and design of indoor distributed localization system. Presented solution was designed primarily for swarm system and was aimed to cooperate with other environment recognition system used for mobile robots. Usage of network connected set of processes, running across simple Linux based computerized platform, provides high redundancy and platform interoperability.


2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986038
Author(s):  
Huang Yiqing ◽  
Wang Hui ◽  
Wei Lisheng ◽  
Gao Wengen ◽  
Ge Yuan

This article presented a cooperative mapping technique using a novel edge gradient algorithm for multiple mobile robots. The proposed edge gradient algorithm can be divided into four behaviors such as adjusting the movement direction, evaluating the safety of motion behavior, following behavior, and obstacle information exchange, which can effectively prevent multiple mobile robots falling into concave obstacle areas. Meanwhile, a visual field factor is constructed based on biological principles so that the mobile robots can have a larger field of view when moving away from obstacles. Also, the visual field factor will be narrowed due to the obstruction of the obstacle when approaching an obstacle and the obtained map-building data are more accurate. Finally, three sets of simulation and experimental results demonstrate the performance superiority of the presented algorithm.


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