Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

2011 ◽  
Vol 30 (3) ◽  
pp. 281-292 ◽  
Author(s):  
Ji-Gong Li ◽  
Qing-Hao Meng ◽  
Yang Wang ◽  
Ming Zeng
2013 ◽  
Vol 441 ◽  
pp. 796-800
Author(s):  
Chun Shu Li ◽  
Zhi Hua Yang ◽  
Gen Qun Cui ◽  
Bo Jin

Aiming at the odor source localization in an obstacle-filled wind-varying indoor environment, a new method based odor source localization algorithm for a single mobile robot is proposed. With the information of the wind and the concentration gradient, Wasps can find odor source in a short time. However, it is very difficult for mobile robots to mimic the behaviors of wasps exactly. So, besides the bionics, BP neural network is adopted for the mobile robot to find the odor source. The control strategies for the plume-tracing mobile robot are proposed which include the intelligent plume-tracing algorithm and the collision avoidance algorithm based on improved potential grid method. The algorithms were integrated to control the robot trace plumes in obstructed indoor environments. Experimental results have demonstrated the capability of this kind of plume-tracing mobile robot.


2020 ◽  
pp. 1018-1029
Author(s):  
Jongil Lim ◽  
Seokju Lee ◽  
Girma Tewolde ◽  
Jaerock Kwon

Identifying the current location of a robot is a prerequisite for robot navigation. To localize a robot, one popular way is to use particle filters that estimate the posterior probabilistic density of a robot's state space. But this Bayesian recursion approach is computationally expensive. Most microcontrollers in a small mobile robot cannot afford it. The authors propose to use a smartphone as a robot's brain in which heavy-duty computations take place whereas an embedded microcontroller on the robot processes rudimentary sensors such as ultrasonic and touch sensors. In their design, a smartphone is wirelessly connected to a robot via Bluetooth by which distance measurements from the robot are sent to the smartphone. Then the smartphone takes responsible for computationally expensive operations like executing the particle filter algorithm. In this paper, the authors designed a mobile robot and its control architecture to demonstrate that the robot can navigate indoor environment while avoiding obstacles and localize its current position.


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