基于双编码器的多目标轨迹规划
Multi-Objective Trajectory Planning with Dual-Encoder
AI总结：
针对机械臂时间-加加速度最优轨迹规划延迟大的问题，提出一种两阶段方法，结合双编码器transformer模型生成初始轨迹并用序列二次规划细化，大幅缩短规划时间并降低目标函数值。
摘要：
时间-加加速度最优轨迹规划对于提升机械臂在动态任务中的性能至关重要。传统方法依赖于求解复杂的非线性规划问题，导致在生成优化轨迹时产生显著延迟。在本文中，我们提出了一种两阶段方法来加速时间-加加速度最优轨迹规划。首先，我们引入了一个基于双编码器的transformer模型来建立良好的初始轨迹。随后，通过序列二次规划对该轨迹进行细化，以提高其最优性和鲁棒性。我们的方法在减少轨迹规划时间方面比现有最优方法高出79.72%。与现有方法相比，我们的方法缩小了最优性差距，目标函数值最多降低了29.9%。
英文摘要：
Time-jerk optimal trajectory planning is crucial in advancing robotic arms' performance in dynamic tasks. Traditional methods rely on solving complex nonlinear programming problems, bringing significant delays in generating optimized trajectories. In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning. Firstly, we introduce a dual-encoder based transformer model to establish a good preliminary trajectory. This trajectory is subsequently refined through sequential quadratic programming to improve its optimality and robustness. Our approach outperforms the state-of-the-art by up to 79.72\% in reducing trajectory planning time. Compared with existing methods, our method shrinks the optimality gap with the objective function value decreasing by up to 29.9\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
