Unmanned aerial vehicles (UAVs) have been widely deployed in many applications such as transportation, data collection, monitoring, or tracking objects. Nowadays, numerous missions require UAVs to operate in a large area or to complete missions in a stringent period of time. Using a single UAV may not meet theperformance requirements because of its small size and limited battery. In this situation, multiple Unmanned Aerial Vehicles (UAVs) have emerged as an effective measure that can address these limitations. A group ofUAVs cooperatively working together could offer a solution that is more efficient and economical than usinga powerful UAV alone. To better utilizing the multiple-UAVs system, control of formation UAVs is a critical challenge that needs to overcome. Therefore, formation control has become an active research topic that gains great attention from researchers. Extensive research efforts have been dedicated to studying the formation control problem with numerous control protocols which have been proposed. This paper reviews the profound studies on formation control in literature. Each approach is investigated based on different criteria, which highlights its distinct merits and demerits. The comparison is provided to facilitate the readers in their future researches in the field of formation control. Finally, some open challenges and research directions are also discussed.

/pdf/formation-control-algorithms-for-multiple-uavs-a-28el9y16lk.pdf

Formation Control Algorithms for Multiple-UAVs: A Comprehensive Survey

Drones have been greatly developed to facilitate the progress of various industries. The safe operation of drones in the urban airspace is critical to ensure a reliable and high-efficient urban air traffic system. This work presents a fusion scheme to achieve autonomous drone collision-free path planning considering static obstacles and dynamic threats detected. Firstly, a 3D voxel jump point search (JPS) based path planning model is developed to generate the static collision-free reference path. With the optimization, the reference path is then de-diagonalized, reconstructed, and smoothed to obtain the desired path. Subsequently, a local collision resolution method is proposed to avoid near mid-air collision of the dynamic threats. The method is based on the Markov decision process (MDP) to implement real-time dynamic collision avoidance. Simulations are conducted to verify the performance of the proposed model. The simulation results demonstrate that the proposed model is effective to achieve the autonomous path planning and real-time collision resolution of multirotor drones.

3D path planning and real-time collision resolution of multirotor drone operations in complex urban low-altitude airspace

Aiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

/pdf/uav-formation-obstacle-avoidance-control-algorithm-based-on-2bwe79pwzw.pdf

UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

In this study, a new joint formation combined with a two-part underwater towed vehicle (towfish) with multiple autonomous underwater vehicles (AUVs) was investigated. A triangular structure formation was established based on graph theory, in which the main point is the secondary towed vehicle acting as the “leader,” and the other two points are AUVs acting as “followers.” The excellent real-time performance and high flexibility of the towfish is highlighted, and the communication delay and fixed routine of AUVs can be avoided simultaneously. As to the obstacle avoidance, the null-space-based behavioral approach is proposed. On the basis of this approach, the formation task moving to the target is decomposed into different subtasks, and the obstacle avoidance subtask is set as the highest priority. The vector of the low-level task is projected to the null space of the high-level task vector, and the integrated task output is used as the final output function. The low-level task is partially or completely accomplished while handling the higher task; therefore, the mutual conflict between different level targets can be avoided. Moreover, the corresponding task functions are designed in accordance with different subtask priorities. The comprehensive output function of formation motion is deduced and established to ensure that obstacles can be avoided effectively. Furthermore, simulation results demonstrate the effectiveness and feasibility of the proposed method in a complex underwater environment with obstacles.

/pdf/joint-formation-control-with-obstacle-avoidance-of-towfish-19z8brpaw2.pdf

Joint Formation Control with Obstacle Avoidance of Towfish and Multiple Autonomous Underwater Vehicles Based on Graph Theory and the Null-Space-Based Method

The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain-such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that-compared with the existing methods based on APF-the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios. 

/pdf/piecewise-potential-field-based-path-planning-method-for-17p7851v.pdf

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Researches on unmanned aerial vehicle (UAV) formation control are attracting more and more researchers' attention. Obstacle avoidance is a key problem of UAV formation. In this paper, an improved artificial potential field (APF) method is proposed for solving the problem of UAV formation control with obstacle avoidance in a complex environment. Point-mass models with kinematic constraints have been employed for UAVs, and autopilots are modelled as first-order systems. Structural constraints of desired formation configuration are considered in the design of the attraction potential field among UAVs, which ensures the convergence of UAVs to a given formation configuration for any initial condition. Furthermore, for UAVs flying at high speeds, an improved APF method combined with formation division method is introduced to realize more flexible formation obstacle avoidance in congested settings where many static and dynamic obstacles exist. Finally, simulations of UAV formation keeping and obstacle avoidance are presented. The proposed methods enable UAVs with different initial states to build expected formation, track the desired trajectory and avoid collisions and obstacles during formation flight. The simulation results demonstrate the effectiveness of the improved APF method.

UAV formation control with obstacle avoidance using improved artificial potential fields

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UAV formation control with obstacle avoidance using improved artificial potential fields