Motion Waste: Why Most Industrial Robots Still Burn Energy on Bad Paths
Week 38 counted the fleet-wide load and opened a humanoid’s battery. Week 39 goes deeper, into the trajectory, where the same robot can burn 20%+ extra energy for the identical job depending only on where it’s placed and how it’s programmed.
A six-axis arm spends most of its electricity moving mass through poorly chosen joint space: overshoot, sharp acceleration, detours, high-torque postures. ABB found 70%+ of its robots’ operational carbon footprint is electricity used in operation, and the path is where operation lives. The evidence is consistent: a measured ~23% difference on a real ABB IRB 1600 from workspace placement alone (2.85 vs 3.52 kWh, same task); ~10–15% from multi-objective trajectory work; 20%+ from deep-learning planners; 15%+ from mobile-robot path work, all without touching the hardware.
Plants still run bad paths because a path is taught once for collisions and takt, energy is rarely a teach-pendant field, and nobody reopens validated motion for kWh, locking in waste for the life of the program. Motion waste is a management-system problem: the robot executes whatever path leadership accepts as “done.” Your action this week: ask whoever signed off your last cell when the path was last checked for energy, not just collisions and cycle time. Full breakdown at renegrywnow.com.
Reflection questions
When was any of your robot paths last checked for kWh, rather than only collisions and cycle time?
Are you buying new hardware to “save energy” while leaving the taught paths that cause the waste untouched?
Does “maximum speed everywhere” run on your non-constraint cells, quietly burning energy for no throughput gain?
Keywords: Motion Waste, Robot Energy, Trajectory Optimization, Path Planning, Workspace Placement, Energy Efficiency, ABB IRB 1600, Digital Twin, Teach Pendant, Physical AI
Series: Energy Dominance · Week 39 · Part I
Next: Part II — Digital Twins That Cut kWh, Not Just Cycle Time.
