Abstract
Robotic manipulators rely on feedback obtained from rotary encoders for control purposes. This article introduces a vision-based feedback system that can be used in an agricultural context, where the shapes and sizes of fruits are uncertain. We aim to mimic a human, using vision and touch as manipulator control feedback. This work explores the use of a fish-eye lens camera to track a SCARA manipulator with coloured markers on its joints for the position estimation with the goal to reduce costs and increase reliability. The Kalman Filter and the Particle Filter are compared and evaluated in terms of accuracy and tracking abilities of the marker’s positions. The estimated image coordinates of the markers are converted to world coordinates using planar homography, as the SCARA manipulator has co-planar joints and the coloured markers share the same plane. Three laboratory experiments were conducted to evaluate the system’s performance in joint angle estimation of a manipulator. The obtained results are promising, for future cost effective agricultural robotic arms developments. Besides, this work presents solutions and future directions to increase the joint position estimation accuracy.
Highlights
R OBOTIC manipulators usually rely on data obtained from rotary encoders to determine their joint angles and to relay it to a closed-loop control system
The system, composed of a Selective Compliance Articulated Robot Arm (SCARA) manipulator, a raspberry pi camera, a fisheye lens and coloured markers, was able to estimate the joint angles with some errors
The results proved that a system like the proposed one can calculate the joint angles of a co-planar manipulator and reduce the number of sensors required
Summary
R OBOTIC manipulators usually rely on data obtained from rotary encoders to determine their joint angles and to relay it to a closed-loop control system. Zhang et al [6] proposed an inversion-free image-based visual servoing system for manipulators with an eye-in-hand camera configuration using neural networks. Their system was theoretically effective at converging feature errors to near-zero values while within the manipulator’s velocity and position limits; the authors propose implementing the proposed system on a physical manipulator. Wang et al [7] developed an adaptive visual servoing system for soft manipulators Their control system is based on piecewise-constant curvature kinematic and does not require the true values of the manipulator link lengths and the target positions. The authors concluded that their experimental results proved the effectiveness of the approach for practical applications
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