This Research Topic seeks to bring together pioneering research that harnesses computational intelligence to enhance sustainability in automation and robotics. This Topic will cover innovative methodologies and real-world applications that promote energy optimization and sustainability in robotic systems. Key topics include optimal control strategies utilizing advanced algorithms and predictive methods for real-time energy consumption adjustment, intelligent motion planning through evolutionary algorithms and machine learning, and data-driven approaches for extracting insights and optimizing energy use in automated systems. Researchers and practitioners are invited to contribute their findings on the development of eco-friendly and intelligent robotic systems, advancing the field toward a more energy-efficient future.

The Research Topic "Computer Intelligence for Energy-Efficient Robotic Systems" aims to explore sustainable and energy-efficient solutions in robotics and automation. Addressing the environmental and economic impacts of robotic energy consumption, the Topic will focus on several key goals:
1. Advanced Control Strategies: Develop innovative algorithms and predictive methods to optimize real-time energy use.
2. Intelligent Motion Planning: Use evolutionary algorithms, machine learning, and reinforcement learning for adaptive, energy-aware motion planning.
3. Data-Driven Decision Making: Apply big data analytics and machine learning to derive insights and enhance energy efficiency in robotics.
4. Real-World Applications: Showcase practical implementations demonstrating the benefits of computational intelligence in various industries.
This Topic seeks to advance the field towards a sustainable future through collaboration and innovation.

Some themes for the Research Topic include, but are not limited to:
1. Optimal Control Strategies for Energy Minimization:
• Advanced control algorithms utilizing computational intelligence for energy optimization.
• Predictive control methods for real-time adjustment of robotic systems to minimize energy consumption.
• Hybrid control strategies combining computational intelligence and traditional control for improved energy efficiency.
2. Intelligent Energy-Aware Motion Planning:
• Evolutionary algorithms for optimizing energy-efficient motion planning.
• Machine learning approaches for predicting and optimizing robot trajectories with energy considerations.
• Reinforcement learning techniques for adaptive and energy-aware motion planning.
3. Data-driven Approaches for Robotics and Automation:
• Big data analytics for extracting insights on energy usage in automated manufacturing.
• Data-driven decision-making processes for optimizing energy consumption in robotic systems.
• Machine learning applications for predictive maintenance and energy-efficient operation.

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Frontiers in Robotics and AI

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