Designing a Robot Stand: Why Dynamic Forces Will Ruin Your Day (If You Ignore Them)

· 3 min read · 765 words
claude-code automation testing debugging

A 30kg payload sitting on a robot arm is manageable. That same payload swinging at full speed and slamming to a halt? That’s 3-5 times the static load trying to rip your mounting stand apart. Today I started planning a MATLAB simulation to figure out exactly how bad it gets.

The Problem

I’m building a mobile platform for a FANUC CRX-30iA collaborative robot arm as part of a university project. The arm needs a stand, and that stand needs to survive not just the weight of the robot, but every dynamic force generated when it accelerates, decelerates, and whips through its workspace.

The physics are unforgiving. When axis 1 rotates at its maximum 120°/s and brakes hard, the inertia of the entire arm creates a moment at the base that dwarfs static gravity loads. I need to know the worst-case forces and moments in every direction—Fx, Fy, Fz, Mx, My, Mz—to design a stand that won’t flex when the robot moves.

    ┌─────────────┐
    │   Payload   │──→ Acceleration
    └──────┬──────┘
           │
    ┌──────┴──────┐
    │  Robot Arm  │
    │  (moving)   │
    └──────┬──────┘
           │
    ═══════╪═══════  ← Base mounting plane
           │
      Fx ←─┼─→      Reaction forces
           │ Fz     and moments at
      My ←─○─→ Mx   the stand interface
           ↓
    ┌──────────────┐
    │    Stand     │
    └──────────────┘

Getting the Coordinate Frame Right

When I described what I wanted—a MATLAB script to simulate base forces—Claude immediately flagged a detail I’d glossed over: which reference frame?

For structural analysis, the reactions need to be in the base frame (fixed to the stand), not the tool frame or any intermediate link. FANUC uses a right-hand coordinate system with Z pointing up through axis 1. It’s the kind of thing that’s easy to overlook until you’re debugging sign errors in your moment calculations at 2 AM.

Mining the Data Sheet

The CRX-30iA specs became the foundation for simulation planning:

  • Axis 1 max speed: 120°/s — Creates the largest moment arm for payload inertia
  • Max payload: 30kg at reduced reach, 25kg at full 1249mm extension — We’ll use 25kg for worst-case analysis
  • Robot mass: 40kg — The arm itself contributes to base reactions during acceleration
  • Repeatability: ±0.03mm — High gear ratios mean significant reflected inertia

What the data sheet doesn’t provide: mass distribution along each link and moments of inertia. For a first pass, we can treat each link as a uniform cylinder and estimate from there. Conservative inertia estimates mean conservative force predictions—better to overdesign than watch the stand flex under load.

The Approach That Almost Wasn’t

My initial plan seemed reasonable: find the maximum acceleration of each axis, multiply by payload inertia, done. Claude pushed back hard.

The worst case isn’t all axes at max acceleration simultaneously—the robot controller won’t allow that. Real trajectories coordinate the axes, and the base moment depends heavily on arm configuration. Axis 1 rotating with the arm fully extended creates a far larger moment than the same rotation with the arm tucked in.

This changed everything. Instead of computing forces at a few static configurations, the simulation needs to:

  1. Generate representative trajectories through the workspace
  2. Compute inverse dynamics along those trajectories
  3. Track maximum forces and moments, plus which configurations produce them

More work, but the right work. Designing for impossible all-axes-max-acceleration loading would waste material solving a problem the robot controller already prevents.

The Implementation Plan

The MATLAB work starts with defining the kinematic chain using Denavit-Hartenberg parameters from the data sheet. Then: estimated mass properties for each link, trajectory generation using MATLAB’s Robotics Toolbox, and inverse dynamics to get joint torques. The Jacobian transpose transforms those torques into base reactions.

The deliverable: maximum forces and moments in each direction, paired with the arm configurations that produce them.

The Unexpected Value of Pushback

What surprised me most about this session wasn’t the technical content—it was having my approach challenged. When I described a simpler analysis method, Claude didn’t just execute it. The response identified the limitations and explained why trajectory-based simulation produces better answers.

That’s the pattern I keep finding useful: not “here’s how to do the thing you asked for” but “here’s why the thing you asked for might not be the thing you actually need.” The coordinate frame clarification alone probably saved hours of debugging.

Tomorrow, the actual MATLAB implementation begins. First milestone: base reactions for a simple trajectory, sanity-checked against static loading. The dynamic forces should be larger—but not by orders of magnitude. If they are, something’s wrong with the model.