A Robust and Energy-Efficient Trajectory Planning Framework for High-Degree-of-Freedom Robots

Abstract

Energy efficiency and motion smoothness are essential in trajectory planning for high-degree-of-freedom robots to ensure optimal performance and reduce mechanical wear. This paper presents a novel framework integrating sinusoidal trajectory generation with velocity scaling to minimize energy consumption while maintaining motion accuracy and smoothness. The framework is evaluated using a physics-based simulation environment with metrics such as energy consumption, motion smoothness, and trajectory accuracy. Results indicate significant energy savings and smooth transitions, demonstrating the framework's effectiveness for precision-based applications. Future work includes real-time trajectory adjustments and enhanced energy models.