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.

摘要

创新点

本文的创新点体现在以下三个方面:

• (1) 提出了一种简单实用的跟踪算法来连续地检测和跟踪机器鱼;

• (2) 提出了一种机器鱼板载控制系统与运动测量系统之间的时间同步算法, 利用机器鱼的多连杆结构和关节运动规律解决时间同步问题;

• (3) 采用平滑样条函数拟合位姿曲线, 以抑制噪声的影响, 并基于此获取运动速度。