Obstacle avoidance and collision avoidance of UAV swarm based on improved VFH algorithm and information sharing strategy

An obstacle avoidance and collision avoidance approach based on an improved VFH algorithm with Apollonius circle and information sharing strategy is put forward with the goal of guiding UAV swarms in formation flight in an unknown complex environment. For the second-order consensus protocol, a cooperative control law with the virtual leader is presented. For collision avoidance, the vector field histogram algorithm with Apollonius circle is proposed, and the obstacle sharing strategy is proposed to avoid obstacles in advance and solve the formation separation problem. The Apollonius circle is introduced to prevent the entanglement problem brought on by symmetry conflicts, and this approach aims to address the problem of collision avoidance among UAVs by updating the polar coordinate histogram based on the VFH algorithm in accordance with the motion state and relative position of other UAVs in the communication neighborhood. The equivalent detection range of UAVs in the swarm connected topology can be increased by sharing the locations of identified obstacles, improving total obstacle avoidance. Also, to reduce repetitive data interactions and communication loads when sharing obstacle information, a data offset mechanism is proposed, which stores the obstacle information in a chronological queue and sets a data offset in the queue for each UAV in the communication neighborhood to indicate the degree of synchronization with the data in the queue. Finally, simulation results show the effectiveness of the algorithm in collision avoidance and real-time obstacle avoidance of UAV swarm. Additionally, a data offset mechanism is suggested, which stores obstacle information in a chronological queue and sets a data offset in the queue for each UAV in the communication neighborhood to represent the degree of synchronization with the data in the queue, to reduce repetitive data interactions and communication loads when sharing obstacle information. Finally, simulation results demonstrate the algorithm's effectiveness in real-time obstacle avoidance and collision avoidance for UAV swarms.