Over the past decade, unmanned aerial vehicles (UAVs) have demonstrated increasing attention and promise. They demonstrate great potential for application in both civilian and military fields. In this context, collision-avoidance (CA) approaches play an extremely important role in collaborative and noncollaborative UAVs in a common airspace for reliable and safe operation. In this article, we give an analysis and description of the CA functions, including the CA process, state sensing, conflict detection, and conflict resolution. Then, an overview of the recent progress in CA technologies is systematically provided, particularly in terms of sensing techniques, conflict detection, and conflict resolution. The state-sensing methods include ground-based and air-based sensing technologies, and cooperative and non-cooperative sensing. The conflict-detection methods contain conflict detection with certainty and without certainty. The conflict-resolution methods include methods for individual UAVs and for UAV formation. In addition, several challenging problems are introduced to develop a lucid research direction. This review gives a unique perspective and contributes toward the further development of CA approaches for UAVs.

In this paper, we study the problem of conflict detection and resolution for unmanned aerial vehicle (UAV) swarms. Specially, we propose a distributed conflict detection and resolution method for multi-UAVs in formation based on consensus algorithm and strategy coordination. When encountering threat swarms, the UAVs in one swarm act as one unit and are together treated as one control object. Each swarm in conflict selects three candidate collision avoidance maneuvers from the preset strategy pool, generates the corresponding planned trajectories with an uncertainty trajectory modeling, and then broadcasts and shares them. All of the swarms in conflict coordinate and determine an optimal combination of strategies. When a collision is imminent, the primary strategy is activated. Each swarm adopts a “leader-follower” strategy, where the leader UAV is regarded as the controller and flies independently, and the others follow the leader UAV. During motion, a decentralized consensus algorithm is adopted for agents to converge to their positions for the desired formation and to maintain a stable geometric configuration. A temporal and spatially integrated conflict-detection model is improved, especially for UAV swarms. A token-allocation strategy is especially improved for distributed coordination to resolve the partial knowledge of the airspace condition. Damping of the coordination is proposed to address data dropouts and transmission delays. Two typical scenarios are conducted to test the methodology proposed in this paper. The simulation result demonstrates the effectiveness and rationality of the proposed methodology.

With the rapid advancement of UAV technology, the utilization of multi-UAV cooperative operations has become increasingly prevalent in various domains, including military and civilian applications. However, achieving efficient coordinated rounding-up of multiple UAVs remains a challenging problem. This paper addresses the issue of collaborative drone hunting by proposing a decision-making control model based on deep reinforcement learning. Additionally, a shared experience data pool is established to facilitate communication between drones. Each drone possesses independent decision-making and control capabilities while also considering the presence of other drones in the environment to collaboratively accomplish obstacle avoidance and rounding-up tasks. Furthermore, we redefine and design the reward function of reinforcement learning to achieve precise control of drone swarms in diverse environments. Simulation experiments demonstrate the feasibility of the proposed method, showcasing its successful completion of obstacle avoidance, tracking, and rounding-up tasks in an obstacle environment.

This paper presents a novel collision avoidance (CA) algorithm for a cooperative fixed-wing unmanned aerial vehicle (UAV). The method is based on maneuver coordination and planned trajectory prediction. Each aircraft in a conflict generates three available maneuvers and predicts the corresponding planned trajectories. The algorithm coordinates planned trajectories between participants in a conflict, determines which combination of planned trajectories provides the best separation, eventually makes an agreement on the maneuver for collision avoidance and activates the preferred maneuvers when a collision is imminent. The emphasis is placed on providing protection for UAVs, while activating maneuvers late enough to reduce interference, which is necessary for collision avoidance in the formation and clustering of UAVs. The CA has been validated with various simulations to show the advantage of collision avoidance for continuous conflicts in multiple, high-dynamic, high-density and three-dimensional (3D) environments. It eliminates the disadvantage of traditional CA, which has high uncertainty, and takes the performance parameters of different aircraft into consideration and makes full use of the maneuverability of fixed-wing aircraft.

Autonomous cooperative flight of multiple UAVs in complex and unknown environments such as jungles poses significant challenges. Existing approaches often rely on wireless communication or omnidirectional vision to achieve cooperation. This paper presents a dual-horizon nonlinear model predictive control (NMPC) scheme for vision-limited multi-UAV systems in cluttered scenarios. The proposed scheme serves as a local path planner for UAVs equipped with forward vision limited to a 160degree field of view, enabling distributed cooperative flight, obstacle avoidance, and navigation. To expand the observation area, UAVs randomly oscillate left and right around their direction of motion. The NMPC employs an objective function incorporating distributed cooperative flight and obstacle avoidance considerations during navigation. This objective function consists of short-term reactive objectives to maintain formation and consistent distance from neighbors, and long-term predictive objectives for planning collision-free trajectories. CasADi, an open-source tool for nonlinear optimization and algorithmic differentiation, is utilized to solve the NMPC problem. The trajectory calculation is performed using initial conditions and fed as additional input to the NMPC. The first control signal of the predictive control sequence is then transmitted to the autopilot for UAV control. The proposed NMPC approach provides real-time solutions with a 10 Hz operating frequency and a two-second prediction horizon, making it suitable as a local path planner. The effectiveness of the proposed approach is evaluated in three challenging scenarios using Gazebo-ROS.