A Robust and Energy-Efficient Trajectory Planning Framework for High-Degree-of-Freedom Robots
Sajjad Hussain, Md Saad, Almas Baimagambetov, Khizer Saeed
TL;DR
Problem: energy-intensive trajectory planning for high-DOF robots. Approach: integrate non-linear sinusoidal trajectories with velocity scaling via an energy-aware cost function, supplemented by cubic-spline adaptation and collision avoidance. Contributions: formulation of $C_{energy}$ and adaptive trajectory generation, plus empirical demonstration of energy savings and smoothness in a physics-based PyBullet simulation. Impact: enables efficient, precise control for industrial and precision robotics, with future work on real-time adjustments and richer energy models.
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.