Abstract: In order to improve the efficiency of existing random search methods, a search strategy was proposed to reconnoiter a hidden-object of the reconnaissance mission. The strategy, called combined planning path and combined sensor search method (CP&CS), was designed mainly to be applied to unmanned ground vehicles equipped with different detection range sensors. In CP&CS, a sensor-based goaled rapidly-exploring random tree was arranged to plan the global path towards the hidden object. Besides, a heuristic A* method was utilized to deal with the narrow channel formed by obstacles. A simulation experiment was designed to validate the proposed strategy. The results show that in a simulation environment with 250 meter radius and occlusions, compared with the path deformation strategy with short-range sensors, the CP&CS method can improve the search efficiency by 3.11 times and shortens the length of planned path by 9.63%. Compared with goaled RRT search strategy with long-range sensors, the CP&CS method can improve the search efficiency by 3.53 times and shortens the length of planned path by 12.06%. The experimental results prove the superiority of the proposed CP&CS strategy in hidden-object search of reconnaissance mission.