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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.17353 |
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| _version_ | 1866914728317550592 |
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| author | Zhang, Beibei Xiang, Tian Mao, Chentao Zheng, Yuhua Li, Shuai Niu, Haoyi Xi, Xiangming Bai, Wenyuan Gao, Feng |
| author_facet | Zhang, Beibei Xiang, Tian Mao, Chentao Zheng, Yuhua Li, Shuai Niu, Haoyi Xi, Xiangming Bai, Wenyuan Gao, Feng |
| contents | 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\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17353 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Objective Trajectory Planning with Dual-Encoder Zhang, Beibei Xiang, Tian Mao, Chentao Zheng, Yuhua Li, Shuai Niu, Haoyi Xi, Xiangming Bai, Wenyuan Gao, Feng Robotics Machine Learning 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\%. |
| title | Multi-Objective Trajectory Planning with Dual-Encoder |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2403.17353 |