Autonomous plume Near-Source search assisted by intermittent visible plume information using finite state Machine and YOLOv3-tiny

Abstract

Since a plume’s visual characteristics have not been fully considered and utilized, the traditional plume near-source search methods lose a large amount of potentially available information, resulting in the low efficiency and reliability of near-source search. This paper proposes a novel plume near-source search approach assisted by the intermittent visible plume information using finite state machine and YOLOv3-tiny for a robot. A finite state machine is used to realize the autonomous behavioral decision-making of the robot according to the obtained visible plume information, olfactory information, and lidar information. In addition, the YOLOv3-tiny network is used to detect the intermittent visible plume information obtained by a depth camera to capture the color and texture of the plume. In order to improve the search reliability of the robot in the olfactory tracking behavior, a whale optimization algorithm is utilized to enable the robot to simulate the hunting behavior and the social mechanism of the whale group and conduct olfactory tracking of the plume. The experimental results show that this proposed plume near-source search approach achieves the success rates of 91.67% and 83.33% in windless and windy environments respectively, and the average total running time is 113.8 s and 159.6 s in the experimental environment with obstacles, respectively.