Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight
This work addresses the challenge of generating optimal trajectories for autonomous quadrotors in dense, limited spaces, representing an incremental improvement in efficiency and scalability for robotics applications.
The paper tackles real-time trajectory planning for aggressive quadrotor flight by proposing an alternating minimization framework that optimizes piecewise polynomial trajectories, achieving superior computational efficiency and spatial-temporal optimality with benchmark results showing outperformance of state-of-the-art methods.
With much research has been conducted into trajectory planning for quadrotors, planning with spatial and temporal optimal trajectories in real-time is still challenging. In this paper, we propose a framework for generating large-scale piecewise polynomial trajectories for aggressive autonomous flights, 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 conditions and time durations of trajectory pieces. In each minimization phase, 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 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 constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art methods regarding efficiency, optimality, and scalability. Aggressive flight experiments in a limited space with dense obstacles are presented to demonstrate the performance of the proposed algorithm. We release our implementation as an open-source ros-package.