Ahstract-Achieving time-optimal flight in real time for multi-drone systems presents significant challenges, particularly in scenarios requiring rapid responses or aggressive maneuvers. This paper introduces a novel framework that bridges the gap between time-optimal polynomial trajectory generation and optimal control, facilitating efficient online replanning (100 Hz onboard) for multiple quadrotors. Specifically, the proposed method leverages a neural network to learn optimal time allocations for polynomial trajectories, which are then integrated with Model Predictive Contouring Control to fully exploit the dynamics of quadrotors. We further extend this approach to multi-drone systems, enabling collaborative high-speed flight with reciprocal collision avoidance. We benchmark the time-optimal performance and computational efficiency of our method in a drone racing scenario and demonstrate its effectiveness in agile cooperative flight within more constrained simulation and real-world environments. The results demonstrate that the proposed method achieves agile waypoint traverse at a speed of up to 19 m/s in simulation and up to 9 m/s in two-drone real-world scenario. [video44https://www.youtube.com/watch?v=KE97sKwYpAs]

The flying speed of autonomous quadrotors has increased significantly in the field of autonomous drone racing. However, most research primarily focuses on the aggressive flight of a single quadrotor, simplifying the racing gate traversal problem to a waypoint passing problem that neglects the orientations of the racing gates or implicitly considers the waypoint direction during path planning. In this paper, we propose a systematic method called Pairwise Model Predictive Control (PMPC) that can guide two quadrotors online to navigate racing gates with minimal time and without collisions. The flight task is initially simplified as a point-mass model waypoint passing problem to provide time optimal reference through an efficient two-step velocity search method. Subsequently, we utilize the spatial configuration of the racing track to compute the optimal heading at each gate, maximizing the visibility of subsequent gates for the quadrotors. To address varying gate orientations, we introduce a novel Magnetic Induction Line-based spatial curve to guide the quadrotors through racing gates of different orientations. Furthermore, we formulate a nonlinear optimization problem that uses the point-mass trajectory as initial values and references to enhance solving efficiency. The feasibility of the proposed method is validated through both simulation and real-world experiments. In real-world tests, the two quadrotors achieved a top speed of$6.1m/s$on a 7-waypoint racing track within a compact flying arena of$5m\times 4m\times 2m$.

Over the past decade, there has been a remarkable surge in utilizing quadrotors for various purposes due to their simple structure and aggressive maneuverability. One of the key challenges is online time-optimal trajectory generation and control technique. This paper proposes an imitation learning-based online solution to efficiently navigate the quadrotor through multiple waypoints with near-time-optimal performance. The neural networks (WN&CNets) are trained to learn the control law from the dataset generated by the time-consuming CPC algorithm and then deployed to generate the optimal control commands online to guide the quadrotors. To address the challenge of limited training data and the hover maneuver at the final waypoint, we propose a transition phase strategy that utilizes MINCO trajectories to help the quadrotor ‘jump over’ the stop-and-go maneuver when switching waypoints. Our method is demonstrated in both simulation and real-world experiments, achieving a maximum speed of 5.6m/s while navigating through 7 waypoints in a confined space of 5.5m × 5.5m × 2.0m [video3]. The results show that with a slight loss in optimality, the WN&CNets significantly reduce the processing time and enable online control for multi-point flight tasks.

We propose a novel hierarchical priority mechanism for deadlock recovery of distributed swarm via on-demand collision avoidance in cluttered dynamic environments. The proposed priority mechanism dynamically assigns certain priority and an optimized detour point for each agent based on its spatial context to avoid deadlocks which are predicted by properly designed deadlock conditions; as a byproduct, this priority mechanism allows us to effectively resolve livelocks as well. The resulting optimization problem is then solved by polar reformulation and alternating minimization methods. Simulation results demonstrate that, in both static and dynamic environments, our method (termed PriDRAM) outperforms the baseline Alternating Minimization Swarm (AMSwarm) method which does not explicitly account for deadlock recovery, with a 10.5% improvement in average smoothness and a 4.8% reduction in flight time. Moreover, for narrow passages, our method shows a superior performance against the Distributed Linear Safe Corridor (DLSC) method, with a more reasonable passing order and an achievement of up to 40% reduction in flight path length. Finally, we verify the efficacy of our proposed method with a Crazyflie 2.1 quadrotor swarm.

Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track in a$35m\times 35m$space with a top speed of 14m/s. Flight tests are performed on two quadrotors passing through 3 waypoints in a$4m\times 2m$flight arena to demonstrate the feasibility of the proposed method in the real world. Both simulations and real-world flight tests show that the proposed method can generate the optimal aggressive trajectories for a swarm of autonomous racing drones. The method can also be easily transferred to other types of robot swarms.