Abstract
The main aim of this article is to provide an example of reproducible research in robotics. Despite the fact that a large number of researchers agree on the need of reproducibility in robotics and artificial intelligence, the practice of reproducible research is still in an embryonic phase. As a matter of fact, IEEE Robotics and Automation Magazine is, at the date of this article submission, the only top-tier robotics publication accepting "reproducible" articles. To this end, we have chosen a very common and traditional problem and approached it with a "first principles" attitude.
Highlights
Many different methods have been proposed in the literature for the control of robotic arms
We propose a platform for reproducible research on robotic arm control
Work We developed a platform for reproducible research and objective comparisons of different methods for a cheap, low-accuracy, lightweight robotic arm
Summary
The main aim of this article is to provide an example of reproducible research in robotics. To validate our reproducible research platform and provide a template methodology for its usage, we have thoroughly compared, in a reproducible way, the performance of simple belief space planning (BSP) and proportional-integral-derivative (PID) controls when applied to a lightweight, low-accuracy, and compliant open source robot arm (i.e., H2Arm). Research Reproducibility Our main goal is to provide a complete software and hardware platform that allows for the statistical replication of our results and experimentation on other, more or less sophisticated control strategies and algorithms We believe this is much needed because real-world comparison of control methods for robot arms is seldom seen in the literature [1], [2]. The system, consisting of an arm and a basic in-hand video camera, was required to Vision Algorithm (C++)
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