Uploaded February 2020 | Updated September 2026, 4 hours ago
Video for the RA-L/IROS 2020 submission.
Preprint: arxiv.org/abs/2002.10629
Code: github.com/ZJU-FAST-Lab/am_traj
Supplementary Material: arxiv.org/abs/2002.09254
In this paper, we propose a framework for generating large-scale piecewise polynomial trajectories for aggressive autonomous flight, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the planning problem, we conduct alternating minimization between boundary condition and time duration of each piece of the trajectory. In each phase of minimization, we leverage the algebraic convenience of the sub-problem to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our proposed method is also provided. Moreover, based on polynomial theory, an extremely fast feasibility check method is designed for various kinds of constraints. By incorporating the method into our alternating structure, a recursive constrained optimization algorithm is constructed for generating optimal feasible trajectory in terms of all representable constraints. Benchmark evaluation shows that our algorithm outperforms state-of-the-art methods regarding efficiency, optimality, and scalability. Aggressive flight experiments in limited space with dense obstacles are presented to demonstrate the performance of our algorithm.
Video for the RA-L/IROS 2020 submission.
Preprint: arxiv.org/abs/2002.10629
Code: github.com/ZJU-FAST-Lab/am_traj
Supplementary Material: arxiv.org/abs/2002.09254
In this paper, we propose a framework for generating large-scale piecewise polynomial trajectories for aggressive autonomous flight, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the planning problem, we conduct alternating minimization between boundary condition and time duration of each piece of the trajectory. In each phase of minimization, we leverage the algebraic convenience of the sub-problem to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our proposed method is also provided. Moreover, based on polynomial theory, an extremely fast feasibility check method is designed for various kinds of constraints. By incorporating the method into our alternating structure, a recursive constrained optimization algorithm is constructed for generating optimal feasible trajectory in terms of all representable constraints. Benchmark evaluation shows that our algorithm outperforms state-of-the-art methods regarding efficiency, optimality, and scalability. Aggressive flight experiments in limited space with dense obstacles are presented to demonstrate the performance of our algorithm.
![Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios [S2]](https://i.ytimg.com/vi/fBwW93Zq9ss/mqdefault.jpg "Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios [S2]
Video for the paper Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios.
[S2] - Simulations and benchmarks.")
