重访机制驱动的多无人机协同动目标搜索方法

To efficiently capture moving targets in unknown regions using multi-UAVs, this paper presents a Revisit Mechanism Driven Cooperative Search Planning (RMD-CSP) method to reduce the probability of missing targets and judgmental errors of the sensors. The multi-UAV cooperative search model, subject to the flight performance constraints, is established to maximize the task execution performance. The search maps (i.e., target probability maps, uncertainty maps, and environment search status maps) are initialized according to the prior information of the target, and then updated using Bayes Criterion according to the detected information by the UAVs. The revisit mechanism based on environment-uncertainty-renewal is customized to reduce the missing-target probability. This mechanism guides the UAVs to search the region that has not been revisited for a long time by enlarging the uncertainty of this region. In addition, the revisit mechanism based on objective-function-weight-renewal is customized to direct the UAVs to revisit the region where a new suspected target is found, and check the existence of the target to reduce the judgmental errors caused by the false-alarm probability of the sensors. Based on the receding horizon framework, the search planning problems are divided into a series of short-horizon planning problems to save computational costs. Simulation studies are conducted under classical mission scenarios to verify the effectiveness of the proposed method. Simulation results demonstrate that the RMD-SCP can generate search paths in seconds for each receding horizon. Compared with the scan-search algorithm and the standard probability heuristic algorithm, the RMD-CSP can guide the UAVs to capture more moving targets with fewer judgmental errors, indicating the effectiveness of the proposed method in improving the efficiency of multi-UAV cooperative search missions.