用残差学习加速机器人低能耗轨迹生成，实时性提升百倍以上。
Real-Time Generation of Near-Minimum-Energy Trajectories via Constraint-Informed Residual Learning
- 基于残差学习，仅优化标准轨迹与最优解的偏差。
- 在训练数据附近达90%以上节能效果，远离时仍保持50%以上。
- 比传统方法快100~1000倍，适合工业实时控制场景。
工业机器人运行能耗高，亟需节能路径规划方法。传统最小能耗轨迹规划依赖非线性最优控制求解，难以满足实时性要求。本文提出一种基于残差学习的近似最小能耗轨迹生成范式，通过学习标准轨迹与最优解之间的调整量，在保留边界条件的同时减少计算负担。相比计算昂贵的OCP规划器，本方法在训练数据附近达到87.3%的节能性能，在远离数据分布区域仍保持50.8%的性能，且速度提升两到三个数量级，显著优于传统方法。
原文摘要 · Abstract (English)
Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time requirements. In this paper, we propose a paradigm for generating near minimum-energy trajectories for manipulators by learning from optimal solutions. Our paradigm leverages a residual learning approach, which embeds boundary conditions while focusing on learning only the adjustments needed to steer a standard solution to an optimal one. Compared to a computationally expensive OCP-based planner, our paradigm achieves 87.3% of the performance near the training dataset and 50.8% far from the dataset, while being two to three orders of magnitude faster.
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