无人机群目标搜索的主动感知方法

为提升蚁群搜索算法在规模大的栅格环境中对未知目标的搜索效率，提出基于蚁群算法的主动感知搜索框架。该框架通过应用历史环境信息来选择无人机的运动方式，并由无人机运动方式和感知域信息得到新的环境信息，从而实现无人机群的智能自动化搜索功能。新方法计算出一种具有探索偏好的未搜索概率，可使无人机搜索时偏向未搜索程度高的栅格，以此来提高算法的搜索能力。同时，以未搜索概率和信息素作为运动方式决策的依据来建立一种新的运动方式选择机制。该机制不仅考虑了目标可能出现的区域，又可兼顾未知区域，从而可实现无目标先验信息条件下的搜索过程。仿真结果表明，此算法在规模大的栅格环境中，与现有算法相比具有更高的搜索效率，并且得到的目标分布信息将更加全面。

Active perception method for UAV group target search

To enhance the search efficiency of the ant colony algorithm for unknown targets in a large-scale grid environment, an active perception search framework based on the ant colony algorithm is proposed. In this framework, the unmanned aerial vehicle (UAV) motion mode was selected using the historical environment information. The new environment information was obtained from the motion mode and sensing domain information of the UAV to enhance the intelligent automatic search function of the UAV group. The new algorithm calculates an unsearched probability with exploration preference to carry out a UAV search with a bias towards the grid with the highest unsearched degree, which improves the algorithm’s searchability. Additionally, based on the unsearched probability and pheromone, a new motion mode selection mechanism was developed. This mechanism considers the possible known and unknown target regions for searching targets with no prior information. The simulation results showed that this algorithm has higher search efficiency and more comprehensive target distribution information than the existing algorithms used in large-scale grid environments.