Energy‑Efficient Spray Robots: Reducing Paint Waste & Cycle Time

As firms seek greener and faster production, energy-efficient robotic spray painting is gaining traction. Emerging research and industrial innovations show that subtle changes in robot motion—not just hardware upgrades—can save 20–30% in energy and cycle time without degrading finish quality for robot spray painting.

What Is Energy-Efficient Robotic Spray Painting?

Traditional robotic painting requires precise trajectory control, holding strict orientation between the spray nozzle and surface normal. However, recent research demonstrates that allowing controlled deviation in orientation—while maintaining uniform paint coverage—lets robots operate faster and consume less energy.

A 2016 IEEE Transactions on Automation Science and Engineering paper by Moe et al. introduced a set-based control framework using a UR5 arm to test this concept. By defining a maximum angle between spray direction and surface normal as a constraint (instead of enforcing exact alignment), the robot could execute trajectories more flexibly. Experimental results showed significant reductions in paint time and energy consumption compared to strict trajectory control.

Trajectory Optimization & Functional Redundancy

Extending the concept, researchers published in Applied Sciences (2020) optimized paint trajectories for the UR5 by tailoring joint motions within redundant degrees of freedom. The robot’s extra axis motions were adjusted to maximize manipulability and minimize motor torque usage—yielding up to 20.8% energy savings while maintaining consistent coating quality  .

This method leverages functional redundancy, meaning that when certain joint orientations aren’t essential for the task, they can be optimized for efficiency. In paint applications, where orientation roll often doesn’t impact coverage, manipulation of these extra degrees of freedom can yield dramatic energy improvements.

Industrial Parallels: What Dürr’s Ecopaint Robots Offer

While most set-based control studies occur in academic contexts, industrial systems like Dürr’s Ecopaint Robot painting stations deliver real-world energy optimization. According to Dürr, their robots use improved drive systems, cooling, and movement algorithms to reduce energy consumption by up to 30% in automotive paint shops. These robots also minimize paint waste and installation time, thanks to modular booth designs and optimized trajectories.

Why These Strategies Matter for Modern Paint Shops

• Less paint waste, lower operating cost

By optimizing spray angles and reducing unnecessary robot motion, shops can cut overspray, solvent usage, and cycle time—delivering direct savings on both materials and energy.

• Faster cycle time at same quality

Set-based control allows robots to paint at higher speeds while allowing small, permissible deviations in nozzle angle—resulting in faster throughput without compromised finish.

• Better robot longevity

Reduced joint torques and smoother trajectories lengthen robot life and reduce maintenance downtime.

Real-World Example: UR5 Energy Optimization Tests

Experiments with set-based control tracked robot energy consumption across four painting tasks. Robots using switched or flexible orientation controls consumed less battery power and completed cycles quicker—proving this isn’t theoretical but applicable with standard industrial arms . Meanwhile, motion design research using functional redundancy showed that planning end-effector orientation profiles reduced energy up to ~21% without altering the paint operation duration .

How to Apply These Insights to Your Shop

1. Audit your robot trajectories

   Do your current spray paths maintain strict normal orientation? Consider relaxing this constraint within acceptable visual quality limits.

2. Leverage motion planning tools

   Integrate elevator planning techniques that vary orientation within a defined cone instead of enforcing rigid angles.

3. Select redundant-capable robots

   Ensure your robot arms (like 6- or 7-axis models) support functional redundancy for orientation flexibility.

4. Partner with integrators

   High-end systems (e.g., Dürr, ABB, FANUC) may integrate these controls in advanced paint booths or offer software updates to existing systems.

5. Measure energy vs. quality trade-offs

   Run pilot tests for energy usage per cycle, throughput, paint consistency, and time to recovery for process validation.

Expected Results: Metrics to Track

Metric	Impact with Optimized Control
Energy consumption per cycle	–20% to –30%
Cycle time efficiency	+10% to +20% faster
Paint material usage	Lower overspray = less waste
Robot wear & maintenance needs	Reduced torque stress
ROI timeframe	Payoff in less than 1 year

What to Watch For

• Visual quality tolerances: Some automotive finishes require tight thickness control (±5 μm). Test thoroughly.

• Control complexity: Set-based algorithms may require specialized software and planning tools.

• Integration requirements: You may need to retrofit motion profiles into existing systems or use middleware.

Conclusion: Efficiency That Doesn’t Sacrifice Quality

Energy-efficient spray robotics goes beyond incremental savings—it’s a transformative approach that uses hardware flexibility, smarter motion planning, and data-backed methods to reduce waste, speed up cycles, and cut energy usage significantly.

Whether you operate high-volume automotive lines or smaller custom finishing shops, these techniques help turn your painting robots into leaner, smarter, and more sustainable systems.