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

2025-03-13

AI Summary

To address the coupled optimization challenge of high energy consumption and motion non-smoothness in high-degree-of-freedom robot trajectory planning, this paper proposes a synergistic optimization framework integrating sinusoidal trajectory parameterization with dynamic velocity scaling. Leveraging physics-based simulation modeling, joint trajectories are explicitly represented using sine functions, while a real-time adaptive velocity scaling strategy jointly optimizes energy consumption and motion smoothness—achieving sub-millimeter accuracy (position error ≤ 0.3 mm). Simulation results demonstrate a 32% reduction in energy consumption and a 41% decrease in trajectory jitter compared to baseline methods, significantly enhancing energy efficiency, robustness, and mechanical longevity. This work constitutes the first systematic integration of sinusoidal parameterization and dynamic scaling for high-dimensional robotic motion planning, establishing a novel, interpretable, and deployable paradigm for energy-efficient, smooth trajectory generation.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Energy-efficient trajectory planning for high-DOF robots
• Minimizing energy consumption while ensuring motion accuracy
• Evaluating framework with energy, smoothness, and accuracy metrics

Innovation

• Sinusoidal trajectory generation for smooth motion
• Velocity scaling to minimize energy consumption
• Physics-based simulation for performance evaluation