Multi-Objective Trajectory Planning with Dual-Encoder
This work addresses a critical bottleneck in robotic arm performance for dynamic tasks, offering significant speed and optimality gains, though it is incremental as it builds on existing optimization methods.
The paper tackles the problem of slow time-jerk optimal trajectory planning for robotic arms by proposing a two-stage approach that uses a dual-encoder transformer model and sequential quadratic programming, resulting in up to 79.72% reduction in planning time and up to 29.9% improvement in optimality.
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\%.