根据人机交互动态自适应优化协作轨迹，提升机器人响应能力。
Adaptive Trajectory Optimization for Task-Specific Human-Robot Collaboration
- 用逆微分Riccati方程动态优化运动轨迹，无需预设路径。
- 神经自适应PID控制器实时调整参数，保持低计算开销。
- 适合需灵活响应人类动作的工业协作场景，如装配与搬运。
本文提出一种面向人机协作的任务特定轨迹优化框架，可根据人类交互动态实现自适应运动规划。与依赖预设期望轨迹的传统方法不同，该框架基于逆微分Riccati方程动态优化协同运动，具备对任务变化和人类输入的适应性。生成的轨迹作为参考输入，驱动神经自适应PID控制器，该控制器利用神经网络实时调节控制增益，有效应对系统不确定性，同时保持低计算复杂度。轨迹规划与自适应控制律的结合确保了系统稳定性与精确的关节空间跟踪性能，无需大量参数调优。数值仿真验证了所提方法的有效性。
原文摘要 · Abstract (English)
This paper proposes a task-specific trajectory optimization framework for human-robot collaboration, enabling adaptive motion planning based on human interaction dynamics. Unlike conventional approaches that rely on predefined desired trajectories, the proposed framework optimizes the collaborative motion dynamically using the inverse differential Riccati equation, ensuring adaptability to task variations and human input. The generated trajectory serves as the reference for a neuro-adaptive PID controller, which leverages a neural network to adjust control gains in real time, addressing system uncertainties while maintaining low computational complexity. The combination of trajectory planning and the adaptive control law ensures stability and accurate joint-space tracking without requiring extensive parameter tuning. Numerical simulations validate the proposed approach.
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