Research on Impact of Planned Path Length and Yaw Cost on Collaborative Search of Unmanned Aerial Vehicle Swarms

To address the unclear impacts of a planned path length and yaw cost on search performance in large-scale Unmanned Aerial Vehicle (UAV) swarm collaborative search scenarios under complex and dynamic environments, a path grid determination algorithm is proposed, transforming the path-planning problem into an optimal waypoint selection problem, enabling UAVs to make rapid decisions using the Particle Swarm Optimization (PSO) algorithm. Simulation experiments were conducted for different planned path lengths with or without the inclusion of the yaw cost, analyzing indicators such as the coverage rate, target capture rate, average capture time, and communication and decision-making consumption. This research was conducted through simulation experiments, and the results demonstrate that increasing the planned path length significantly reduces communication and decision-making consumption while having no notable impact on the coverage rate or search performance. Incorporating the yaw cost slightly improves target search performance but also leads to a minor increase in communication and decision-making consumption.