The MEMS MS Capstone at Duke University

BCN3D MOVEO - A 3D printed open source robotic arm


Welcome to the project page for Team Tandem, where we believe the future is robotics lies in

  • Point-of-service industries
  • Automated medical care
  • Education
  • Other industrial applications

Industrial robots are used to automate menial and/or dangerous tasks performed by humans. These robots do so quickly, with consistent performance. The tasks range from various types of welding to pick-and-place part assembly [1]. There are four main types of robotic arm geometries, rectangular, cylindrical, spherical, and jointed spherical.  The BCN3D is best categorized as a spherical jointed robot. There are various ways to drive these robots, such as planetary gears, belts, hydraulics, etc. Although each one of these methods have their own advantages, they each share a common problem: position control.

Problem Statement

The original problem statement was to study stability of connected closed-loop systems with irregular disturbances. This would be achieved using the two robotic arms, BCN 3D Moveo and WE-R2.4, a. k. a. “Armbot.” The rest of this article will focus on the BCN 3D Moveo.

The vision for this project was to construct an open-source robot and use video feedback to adjust the positioning in a 2D plane. This project aimed to not only research the effects of disturbances of closed-loop systems but to relay various learning objectives. The BCN3D is a 4-axis, belt-driven, spherical-jointed arm that was used to explore position control. The first goal was to implement user input to the BCN3D to position the end effector. Due to imperfections in the mechanics of the system (disturbances), the goal position will often differ from the desired position. The second goal was to use inverse kinematics to achieve the same result communicating with a second bot to perform a shared task.

The MOVEO BCN3D is ideal for both research and education: it has 4 DOF, variable levels of difficulty in the build, inexpensive build cost relative to size, and an easily modifiable gripper.

Project Need Analysis

The need analysis for this project is broken into three sections: functional (how the requirement functions from the customer perspective), and operational (what operations are necessary to keep the project operating), and technical (defining technical issues to address). Review the chart in Table 1 for more information. 

For functional needs, the relation between customer/user and robot was heavily considered. As Chien-Ming Huang, Maya Cakmak, and Bilge Mutlu note in [3], their paper “Adaptive Coordination Strategies for Human-Robot Handovers,” “user experience is another factor that designers must take into account when developing robots for physical collaboration. For instance, in domestic tasks such as doing daily chores such as unloading groceries, users may want to interact with robots at their own pace, as opposed to aiming to maximize team efficiency. Therefore, …solely maximizing task performance may not necessarily result in desirable joint action.” This insight influenced Team Tandem’s decision to provide options for both user-input forward kinematics via Dabble, and simplified path-planning via ROSserial connections to the robot.

For operational needs, it was key to make the robot as amenable to future research as possible. For example, in [4], Chang Tai Kiang, Andrew Spowage, and Chan Kuan Yoong write a meta-analysis of control strategies for flexible manipulators. This apt synthesis may be useful for students to use when making the Moveo a platform for research–the Moveo’s grippers were not fully developed during Fall 2021 semester, and may incorporate some of the noteworthy control techniques the authors propose. They remark that “conventional robotic manipulators have been designed to have maximum stiffness so as to achieve minimum vibration and good positioning accuracy of the end-effector… [however, flexible link manipulators] are designed to be lightweight, higher payload-to-robot-weight ratio, and higher operational speed.” This opportunity is open for students continuing the project past Fall 2021.

To wrap up with technical needs, the Moveo’s original design is ideally reliable. Joseph Cascio writes in [2] that “the resulting optimal [robotic arm] control is highly nonlinear and constrained due to nonlinearities in the robotic arm dynamics and kinodynamic constraints including limits on joint velocities and actuator torques.” In layman’s terms, this mean that the Moveo needs to account for complicated movement appropriately, not with simple linear responses. The project, however, is also designed to be operable by novices. Thus, there will be a trade-off between high-level performance and simplicity. The technical needs are also constrained by a budget.

Table 1: Need analysis chart for the project.

Design Criteria

Figure 1: Design criteria flowchart of ideal system.

The final goal of this project is to have steady communication between two robotic arms, the Moveo BCN3D and the Arm Bot. The design criteria flowchart, i. e. a system flowchart that governs how the robot should interact with its partner and the environment, is shown to the left in Figure 1. Note that this flowchart is idealized and does not exactly summarize the input/output data flow. Also note that, based on research discussions with a department robotics expert, artificial disturbances are not necessary for this project, in its preliminary stages. 

Key desired outcomes:

  • Accepts commands for forward/inverse kinematics
  • Responds to artificial disturbance introduced into system
  • Robots coordinate in tandem
  • System includes live video feedback with new commands to correct position

The purple outlines in Figure 2 denote processes applicable to both WE-R2.4 and the Moveo, while red denotes the Moveo robot only and blue denotes the WE-R2.4 only.

System Decomposition

The system decomposition, Figure 2, is shown in a few colors: blue for WE-R2.4 specific tasks, red for Moveo specific tasks, and purple for tasks that applied to both robotic arms. The work is divided into three disciplines of engineering: computer/software, electrical/hardware, and mechanical. Please consult the tutorial tabs for beginner through advanced steps to see cool information on:

  • Flashing an operating system
  • Using ROS
  • Writing OpenCV code in a Python IDE
  • Designing an electrical circuit for motor drivers, 3D printing
  • Many more fun and interesting tasks! 

Figure 2: System decomposition for the two robotic arms.

Project Timeline

Figure 3: Timeline for the Fall 2021 MEMS Graduate Capstone course, ME 555.

The desired project timeline in Figure 3 is based on weeks in the Fall 2021 semester, with reasonable goals for constructing the Moveo, repairing the WE-R4.2, and joining the robots together to complete a mutual task.

*Note that artificial disturbances are necessary mainly in a perfect environment. Team Tandem estimated that there would be little need to simulate disturbance in the early project stage.

Check out the Full Tutorials here!

Before looking at the tutorials in detail, check out the overall education guide for instructors!


Future Work

Educational Materials

Feel free to use the following education materials in the classroom or at home, and be sure to connect with the authors and share your questions, comments, and progress.

About the authors: Team Tandem

This project page was written by Alexandra Rivera and Asa Guest. Check out their person webpages, which detail their interests and featured subprojects.


[1] BASIC ABOUT INDUSTRIAL ROBOTS. INLEARC, Intelligent e-learning systems in robotics/mechatronics.

1. Basic about Industrial Robots

[2] Cascio, Joseph A., “Optimal Path Planning for Multi-Arm, Multi-Link Robotic Manipulators,” Defense Technical Information Center, December 2008.

[3] Huang, Chien-Ming, et. al., “Adaptive Coordination Strategies for Human-Robot Handovers,” Science and Systems Conference, pp. 1-10, 2015.

[4] Kiang, Chang T., et. al., “Review of Control and Sensor System of Flexible Manipulator,” Journal of Intelligent & Robotic Systems, vol. 77, pp. 187-213, 2015.