Real-Time Generation of Time-Optimal Quadrotor Trajectories with Semi-Supervised Seq2Seq Learning

Abstract

Generating time-optimal quadrotor trajectories is challenging due to the complex dynamics of high-speed, agile flight. In this paper, we propose a data-driven method for real-time time-optimal trajectory generation that is suitable for complicated system models. We utilize a temporal deep neural network with sequence-to-sequence learning to find the optimal trajectories for sequences of a variable number of waypoints. The model is efficiently trained in a semi-supervised manner by combining supervised pretraining using a minimum-snap baseline method with Bayesian optimization and reinforcement learning. Compared to the baseline method, the trained model generates up to 20 % faster trajectories at an order of magnitude less computational cost. The optimized trajectories are evaluated in simulation and real-world flight experiments, where the improvement is further demonstrated.