Energy consumption analysis and optimization in collaborative robots

Area of Science:

• Industrial Automation
• Robotics Engineering
• Energy Management

Background:

• Industrial facilities face significant energy consumption challenges.
• Peak power demand is crucial for electrical infrastructure design and management.
• Collaborative robots, like the UR10, offer flexible automation solutions.

Purpose of the Study:

• To investigate the power and energy consumption of the UR10 collaborative robot.
• To analyze the impact of trajectory parameters on energy use and peak power.
• To develop predictive models for robot energy consumption and peak power.

Main Methods:

• Conducted experiments to collect current consumption data from the UR10 robot API.
• Analyzed the relationship between trajectory programming parameters (speed, acceleration) and energy metrics.
• Developed artificial neural network models to predict power peak and total energy consumption.
• Employed a genetic optimization algorithm to find optimal robot parameters within power constraints.

Main Results:

• Increasing speed and acceleration limits reduces total energy consumed per trajectory.
• Higher speed and acceleration limits lead to increased power peaks.
• A trade-off exists between minimizing consumed energy and managing peak power.
• Predictive models accurately estimated robot energy consumption and peak power.

Conclusions:

• Robot trajectory programming significantly influences energy efficiency and peak power demand.
• Optimized parameters can achieve efficient robot performance while respecting power limitations.
• Artificial intelligence and optimization algorithms are valuable tools for energy-aware robot control.