This paper addresses the trajectory planning problem for search and coverage missions with an Unmanned Aerial Vehicle (UAV). The objective is to devise optimal coverage trajectories based on a utility map describing prior region information, assumed to be effectively approximated by a Gaussian Mixture Model (GMM). We introduce a Model Predictive Control (MPC) algorithm employing a relaxed formulation that promotes the exploration of the map by preventing the UAV from revisiting previously covered areas. This is achieved by penalizing intersections between the UAV's visibility regions along its trajectory. The algorithm is assessed in MATLAB and validated in Gazebo, as well as in outdoor experimental tests. The results show that the proposed strategy can generate efficient and smooth trajectories for search and coverage missions.

翻译：本文研究了无人机在搜索与覆盖任务中的轨迹规划问题。目标是基于描述先验区域信息的效用图（该效用图假设可通过高斯混合模型有效近似）设计最优覆盖轨迹。我们提出了一种采用松弛形式的模型预测控制算法，通过惩罚无人机轨迹上可见区域之间的交叉，防止其重复访问已覆盖区域，从而促进地图的探索。该算法在MATLAB中进行了评估，并在Gazebo及室外实验测试中得到了验证。结果表明，所提出的策略能够为搜索与覆盖任务生成高效且平滑的轨迹。