Annin Robotics Community

  • John Dammeyer@johndammeyer· 10 viewsin General Discussion

    A while ago (a long while) I wanted to build the XY mechanism to duplicate the desktop punch press as shown in this article.  https://www.cnccookbook.com/a-quick-way-to-make-lots-of-holes-with-cnc/

    Well 3D printing has taken over and the need for this or for that matter my 3:1 shear/brake/roller has almost vanished.

    The idea of using the AR3/4 hard to hold a piece of sheet metal and move it under the punch and then pause while an operator pulls on the handle is kind of a neat idea.  And that the Robot can read G-Code implies simple XY positioning along with the halt or pause.

    So suppose two limit switches are set up by the press.  One to detect the back edge and one to detect one side.  That becomes the HOME location for the XY geometry.  After that at hole at X25 Y25 should be easy right?

    Is there any sort of app note or discussion on how to do this?

  • John Dammeyer@johndammeyer· 13 viewsin Robot Builds

    The box is complete with slide out shelf for the power supply.  Not sure yet how I'll finish it.  Paint or maybe get fancy with oak trim.  Once the glue is dry it's time to finish sand it and add the cross grid at the 50mm spacing as suggested by Chris.

  • roland.petrasch@rolandpetrasch· 90 viewsin Robot Builds
    Edited

    🤖 𝗥𝗼𝗯𝗼𝘁𝗶𝗰 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗼𝗿 𝗖𝗼𝗰𝗸𝗽𝗶𝘁: Easy install, setup, first tests for 𝗔𝗥𝟯/𝟰 with 𝗥𝗢𝗦𝟮, 𝗠𝗼𝘃𝗲𝗜𝘁𝟮, 𝗚𝗮𝘇𝗲𝗯𝗼, 𝗟𝗶𝗰𝗵𝘁𝗯𝗹𝗶𝗰𝗸, 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲𝗗𝗕 & more

    Simulate on a Mac/Linux, then run the same software on the real robot. That's the idea behind AR3 Robotic Arm Cockpit, which Richard and I are building for the open-source Annin Robotics AR3 arm.

    What works today (simulation):

    🦾 ROS 2 Jazzy with MoveIt 2 (OMPL and Pilz) and ros2_control

    🏭 A Gazebo Harmonic workcell with table, bins, objects and overhead and wrist cameras

    💻 Runs on Linux and Apple Silicon Macs (Docker, amd64 and arm64), with RViz, RViz in the browser or Lichtblick

    🖥️ AR3 Cockpit, a local web app for install and setup, starting and stopping stacks, monitoring and MoveIt scenarios. It shows the CLI command behind every action

    🗄️ Telemetry and run data captured in TimescaleDB

    💬 An assistant that answers from the project docs, with sources, running locally on Ollama or on Claude.

    Next steps: support of AR4, the real arm, multi-webcam perception, then pick, place and sort.

    On the research menu:

    • Data quality for robotics, high priority: checks and cleansing for sensor data, AI-ready data from simulation and physical-twin runs, and evaluation of AI results, using Grafana and other DQ tools

    • VLA fine-tuning (OpenVLA-OFT, π0 family) on cloud GPUs

    • Language task parsing with a local VLM on a MacBook

    • A learned world model with a sampling-based planner

    • Better perception with lidar and depth cameras

    • NVIDIA Isaac Sim and MuJoCo, as Ekumen shows for the AR4

    The code is on GitHub:

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  • Ian@ian· 88 viewsin General Discussion

    Hello. Can the AR4 push horizontally along the table a 10N force?It's for pushing an ink cartridge into a printer.

    The cartridge pushes in via the side of the machine.

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  • Paolo Gagge@gagge· 1,207 viewsin Robot Builds

    PaMiLoCELL #09 – Brainstorming and Cell Layout - AI SIMULATION SCENARIO

    At this stage of the PaMiLoCELL project, I am carrying out a high-level brainstorming exercise to understand how the robotic cell could evolve before freezing the final layout.

    The objective is not only to define the physical arrangement of the cell, but also to evaluate whether the project can develop into a technically credible and potentially fundable concept.

    Step 0 – AR4 Operating Envelope Analysis

    The starting point is the AR4-MK5 working envelope: maximum reach, inner non-reachable area, joint limits and the difference between theoretical reach and the actually useful workspace.

    Step 1 – Base Cell Layout

    The first concept uses a central inspection/pick area with sorting bins distributed around the perimeter, keeping the first development phase simple and easy to validate.

    Step 3 – Safety + AI Vision Integration

    The next evolution introduces the enclosure, E-stop/reset functions, PLC/safety architecture and AI Vision for object recognition and material sorting.

    Step 4 – Modular Expansion with Conveyor

    A modular/openable side could allow a small conveyor to be added later without redesigning the entire cell, moving from a static demonstrator toward a more dynamic sorting system.

    Beyond the layout itself, I am also starting to look at the project from an innovation and funding perspective:

    • What is genuinely innovative compared with the current state of the art?

    • Which technical problem does the solution solve, and for whom?

    • Can the concept improve existing solutions in performance, flexibility, safety, maintainability or cost?

    • Is there a real market/user need behind the technical idea?

    • How does it compare with existing and competing solutions?

    • What are the main technical and market risks?

    • Which elements could generate proprietary know-how, IP or potentially patentable solutions?

    • Can the concept be validated through measurable technical and economic KPIs?

    This changes the brainstorming process: the goal is no longer only “Can I build it?”, but also “Why should this solution exist, and what measurable value can it create?”

    Questions I am currently asking myself:

    1. What innovative aspects can I introduce compared with the current state of the art?

    2. Is reducing construction and maintenance costs alone enough to create meaningful innovation?

    3. Which measurable benefit would make this solution attractive to a real industrial user or investor?

    4. Which technical result should I demonstrate first to reduce the project risk and strengthen a future funding application?

    Note: The images shown in this post are AI-generated concept simulations created to support layout studies.

    Analyze → Design → Validate → Improve

    #PaMiLoCELL #PaMiLoLAB #Robotics #IndustrialAutomation #RobotCell #AR4MK5 #AnninRobotics #AIVision #MachineVision #FunctionalSafety #PLC #AutomationEngineering #MaterialSorting #Innovation #TechnologyTransfer #OpenSourceRobotics

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  • Paolo Gagge@gagge· 1,209 viewsin Robot Builds

    PaMiLoCELL #08 – Robot Transmission Mechanisms

    While assembling the AR4-MK5 and studying industrial robots, I am realizing that robot motion is not only about motors, software and kinematics.

    Between the motor and the joint there is a fundamental element:

    the mechanical transmission.

    Its architecture directly influences torque, stiffness, inertia, backlash, vibration, accuracy, payload and dynamic behaviour.

    Main transmission families used in robotics include:

    • Gear trains – robust and stiff torque transmission.

    • Planetary gearboxes – compact, efficient and with high torque density.

    • Cycloidal / RV reducers – high stiffness, low backlash and high load capacity, often suited to the main robot axes.

    • Harmonic / Strain-Wave drives – compact, lightweight and with very low backlash, often used where low inertia is important.

    • Timing belt & pulley systems – transfer motion between separated shafts and allow more flexibility in motor positioning.

    • Direct drive – eliminates intermediate transmission stages, but requires high-torque motors.

    A useful distinction is also between robot axes.

    J1–J3 mainly position the arm and normally manage higher loads and moments, so torque, stiffness and load capacity are critical.

    J4–J6 mainly control wrist orientation, where compactness, low mass and low inertia become increasingly important.

    Where does the AR4-MK5 fit?

    The AR4-MK5 uses a hybrid mechanical transmission architecture rather than a single transmission technology.

    During assembly, several axes clearly show a chain based on:

    Stepper Motor → Pulley → Timing Belt → Pulley → Shaft / Joint

    combined with couplers, shafts, keys/keyways, bearings and mechanical reduction stages.

    So the AR4 can be associated mainly with a:

    Timing Belt & Pulley + Mechanical Reduction architecture.

    This allows the motor to be positioned away from the joint and the transmission ratio to be adapted to the required motion.

    The basic principle is:

    Motor: high speed / lower torque → Reduction → Joint: lower speed / higher torque

    or, in simplified form:

    Tjoint ≈ Tmotor × i × η

    where i is the reduction ratio and η the transmission efficiency.

    This is also why small assembly details matter.

    Incorrect belt tension, coupler tightening, key fit, shaft alignment, bearing preload or backlash can later appear as:

    lost motion, vibration, reduced repeatability or positioning errors.

    Key takeaway

    Same 6-axis architecture does not mean the same mechanical behaviour.

    Robot performance depends not only on software and kinematics, but also on what happens mechanically between each motor and joint.

    For PaMiLoCELL, assembling the AR4-MK5 is therefore also a practical way to understand how belts, pulleys, shafts, bearings and reduction systems work together to create robot motion.

    #PaMiLoCELL #PaMiLoLAB #AR4MK5 #Robotics #IndustrialRobotics #RobotMechanics #MechanicalTransmission #TimingBelt #Gearbox #MotionControl #RobotKinematics #MechanicalEngineering #Automation