Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.

Communication is the cornerstone of UAV swarms to transmit information and achieve cooperation. However, artificially designed communication protocols usually rely on prior expert knowledge and lack flexibility and adaptability, which may limit the communication ability between UAVs and is not conducive to swarm cooperation. This paper adopts a new data-driven approach to study how reinforcement learning can be utilized to jointly learn the cooperative communication and action policies for UAV swarms. Firstly, the communication policy of a UAV is defined, so that the UAV can autonomously decide the content of the message sent out according to its real-time status. Secondly, neural networks are designed to approximate the communication and action policies of the UAV, and their policy gradient optimization procedures are deduced, respectively. Then, a reinforcement learning algorithm is proposed to jointly learn the communication and action policies of UAV swarms. Numerical simulation results verify that the policies learned by the proposed algorithm are superior to the existing benchmark algorithms in terms of multi-target tracking performance, scalability in different scenarios, and robustness under communication failures.

The auction mechanism stands as a pivotal distributed solution approach for addressing the task allocation problem in unmanned aerial vehicle (UAV) swarms, with its rapid solution capability well-suited to meet the real-time requirements of aerial mission planning for UAV swarms. Building upon the auction mechanism, this paper proposes a distributed task allocation method for multi-UAV grounded in swarm benefits optimization. The method introduces individual benefit variation to quantify the effect of a task on the benefit of a single UAV, thereby enabling direct optimization of swarm benefit through these individual benefit variations. Within the formulated individual benefit calculation, both the spatial distance between tasks and UAVs and the initial task value along with its temporal decay are taken into account, ensuring a thorough and accurate assessment. Additionally, the method incorporates real-time updates of individual benefits for each UAV, reflecting the dynamic state of task benefit fluctuations within the swarm. Monte Carlo simulation experiments demonstrate that, for a swarm size of 16 UAVs and 80 tasks, the proposed method achieves an average swarm benefit improvement of approximately 2% and 4% over the Consensus-Based Bundle Algorithm (CBBA) and Performance Impact (PI) methods, respectively, thus validating its effectiveness.

This paper tackles the problem of sensing coverage for multiple Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the reciprocal between neighboring UAVs to reduce the oscillation of their trajectories. The proposed reciprocal decision approach, which is performed in three steps, is self-organized, distributed and autonomous. First, in contrast to the traditional method modeled and optimized in configuration space, the sensing coverage problem is directly presented as an optimal reciprocal coverage velocity (ORCV) in velocity space that is concise and effective. Second, the ORCV is determined by adjusting the action velocity out of weak coverage velocity relative to neighboring UAVs to demonstrate that the ORCV supports a collision-avoiding assembly. Third, a corresponding random probability method is proposed for determining the optimal velocity in the ORCV. The results from the simulation indicate that the proposed method has a high coverage rate, rapid convergence rate and low deadweight loss. In addition, for up to 10³-size UAVs, the proposed method has excellent scalability and collision-avoiding ability.

Distributed task scheduling is an ongoing concern in the field of multi-vehicles, especially in recent years; UAV swarm performing complex tasks endows it with new characteristics, such as self-organization, scalability, reconfigurability, etc. This requires the swarm to have distributed rescheduling capability to dynamically include as many unassigned tasks or new tasks as possible, while satisfying tight time constraints. As one of the most advanced rescheduling methods, the Performance Impact (PI)-MaxAss algorithm provides an important reference for this paper. However, its task exchange-based strategy faces the deadlock problem, and the task rescheduling method should not be limited to this. To this end, a new distributed rescheduling method is proposed for UAV swarms, which combines the local task reordering strategy and the improved task exchange strategy. On the one hand, based on the analysis of the fact that the scheduler is unreasonable for individuals, this paper proposes a local task reordering strategy denoted as PI-Reorder, which simply adds the reordering strategy to the recursive inclusion phase of the PI-MinAvg algorithm, so that unassigned tasks or new tasks can be included without relying on the task exchange. On the other hand, from the phenomenon that two or more vehicles occasionally get caught in an infinite cycle of exchanging the same tasks, the deadlock problem of PI-MaxAss is analyzed, which is then solved by introducing a deadlock-free task exchange strategy, where some defined counters are used to detect and isolate the deadlocks. Then, a rescue scenario is used to demonstrate the performance of the proposed methods, PI-Hybrid compared with PI-MaxAss. Monte Carlo simulation results show that, compared with PI-MaxAss, this method can not only increase the number of allocations to varying degrees, but also reduce the average waiting time, while ensuring deadlock avoidance. The methods can be used not only for the secondary optimization of the existing task exchange scheduling algorithms to escape local optima, but also for task reconfiguration of swarm tasks after adding or removing tasks.