Paper Info
Title
Precise planar motion measurement of a swimming multi-joint robotic fish.
Abstract
This paper presents a method for planar motion measurement of a swimming multi-joint robotic fish. The motion of the robotic fish is captured via image sequences and a proposed tracking scheme is employed to continuously detect and track the robotic fish. The tracking scheme initially acquires a rough scope of the robotic fish and thereafter precisely locates it. Historical motion information is utilized to determine the rough scope, which can speed up the tracking process and avoid possible ambient interference. A combination of adaptive bilateral filtering and k-means clustering is then applied to segment out color markers accurately. The pose of the robotic fish is calculated in accordance with the centers of these markers. Further, we address the problem of time synchronization between the on-board motion control system of the robotic fish and the motion measurement system. To the best of our knowledge, this problem has not been tackled in previous research on robotic fish. With information about both the multi-link structure and motion law of the robotic fish, we convert the problem to a nonlinear optimization problem, which we then solve using the particle swarm optimization (PSO) algorithm. Further, smoothing splines are adopted to fit curves of poses versus time, in order to obtain a continuous motion state and alleviate the impact of noise. Velocity is acquired via a temporal derivative operation. The results of experiments conducted verify the efficacy of the proposed method.
Year
DOI
Venue
2016
10.1007/s11432-015-5497-1
SCIENCE CHINA Information Sciences
Keywords
Field
DocType
motion measurement, robotic fish, time synchronization, visual tracking, localization, 运动测量, 机器鱼, 时间同步, 视觉跟踪, 定位
Particle swarm optimization,Computer vision,Synchronization,Motion control,Control theory,Smoothing spline,Eye tracking,Artificial intelligence,Cluster analysis,Bilateral filter,Mathematics,Speedup
Journal
Volume
Issue
ISSN
59
9
1869-1919
Citations 
PageRank 
References 
3
0.52
10
Authors
4
Name
Order
Citations
PageRank
Jun Yuan124423.10
Junzhi Yu2851121.67
Zhengxing Wu38419.10
Min Tan42342201.12