In this article, a multilayered and distributed architecture for mission-oriented miniature fixed-wing UAV swarms is presented. Based on the concept of modularity, the proposed architecture divides the overall system into five layers: 1) low-level control layer; 2) high-level control layer; 3) coordination layer; 4) communication layer; and 5) human interaction layer, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this article is the first work which successfully demonstrates formation flight, target recognition, and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.

For civil applications of Unmanned Aerial Vehicles (UAVs) for search, and emergency, data transmission between UAVs and Ground Control Stations (GCS) is always suffered from long distance, undulating terrains or other interferences. Thus, relaying is a critical enhancing technology in wireless communications for better service quality. In this paper, a planning method for fixed-wing relay UAV (RU) serving to sequential tasks of UAV swarm is proposed. Firstly, considering characteristics of signal propagation, antenna, environment and UAV, the UAV-to-GCS and UAV-to-UAV communication models are designed. And the limits of communication are investigated, which are crucial issues to provide a guarantee for swarm mission. Meanwhile, based on the analysis of relay UAVs requirements during the spatial distribution of tasks, a mixed integer nonlinear programming problem (MINLP) is constructed to solve the approximate number of relay nodes and access locations. Then aiming for the minimum number of relay UAVs, a continuous time-sharing relay UAVs reward model is constructed, and the consensus-based bundle algorithm (CBBA) is adopted to obtain the maximum rewards, including the number of relay nodes, mission sequences and route planning. Finally, the numerical simulation for a typical scenario is conducted to show the performance of the proposed approach.

For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite System) signals are weak due to the crowd being in a swarm or blocked by a forest, mountains, and high buildings in the environment. Thus, relative localization is an indispensable way to provide position information for the swarm. In this paper, we review the status and development of relative localization. It is first assessed that relative localization to obtain spatio-temporal relationships between individuals is necessary to achieve the stable operation of the group. After analyzing typical relative localization systems and algorithms from the perspective of functionality and practicality, this paper concludes that the UWB-based (ultra wideband) system is suitable for the relative localization of robots in large-scale applications. Finally, after analyzing the current challenges in the field of fully distributed localization for robotic swarms, a complete mechanism encompassing the relative localization process and the relationship between local and global localization that can be a possible direction for future research is proposed.

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.

This paper presents a study on short-term cooperative conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). A two-layered CDR mechanism is proposed, which aims at guaranteeing safe separation, minimizing the overall cost of UAVs, and improving computational efficiency. In the first layer, the information from the environment is processed. In the second layer, conflicts among UAVs are resolved by applying the local centralized optimization method, with consideration given to the dynamic constraints of UAVs. This paper studies the safe separation constraints of pairwise conflicts in virtue of a geometry-based method. A heading change and speed change mixed conflict resolution approach is applied. To meet with the online planning requirements, the vectorized stochastic parallel gradient descent-based method is proposed to find the local optimal heading change solutions. The linear safe separation constraints on speeds are derived. A periodicity feature-based method is used to depart the feasible sub-regions for each pairwise conflict. A mixed integer linear programming model is established to find the optimal speed change solutions. The experiments results show that the proposed heading change algorithm could greatly reduce the summation of additional flight distances of UAVs, and influences on air traffic, compared with other short-term algorithms; the computational efficiency of this algorithm satisfies the online planning requirement. Comparing with the existing algorithm, our speed change algorithm reduces the number of feasible sub-regions to 2nc times lower, where nc is the number of pairwise conflict, and therefore, it reduces computation time dramatically.

This paper addresses the onboard locally centralized conflict resolution problem of heterogeneous unmanned aerial vehicles (UAVs) with variable headings. Firstly, the geometric properties of the conflict resolution problem are analyzed in two-dimensional space. By mapping the nonlinear safe separation constraint to the sine value space, a linear constraint for safe separation is established for pairwise conflicting UAVs. Then, a mixed integer linear programming (MILP) model is built to cope with the conflict resolution problem of heterogeneous UAVs, where the objective is to minimize the overall costs. A coordination coefficient λ is defined to enhance the coordination performance between UAVs. The proposed MILP model is extended to the conflict resolution problem of UAVs in 3D space. By using the CPLEX solver, the MILP model can be efficiently solved. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach. The simulation results show that the proposed approach can generate conflict free solutions more efficiently when compared with the existing short-term coordination algorithm. The proposed approach is able to realize UAV coordination in highly congested air traffic and satisfies the onboard conflict resolution requirement.