## 1 Introduction  
Intelligent robots are playing an indispensable role in various industries [1; 2; 3], and a wide variety of intelligent robots are ubiquitously showing remarkable availability in transportation, logistics, agricultural development, and disaster emergency response. An unmanned ground vehicle (UGV) can accurately reach a destination to perform tasks, and they are highly superior in engineering expeditions, military reconnaissance, rescue and relief, etc. [4]. They can help humans operate under harsh conditions, but have significant limitations on environmental perception due to their height. In contrast, unmanned aerial vehicles (UAVs), a major research hotspot today [5; 6; 7; 8; 9], have the advantages of small size, simple structure, flexible operation, vertical takeoff and landing, and more flexible detection of a wide field of view compared to UGVs. The use of unmanned air-ground systems, including UAVs and UGVs, has become an important research area [10; 11; 12] in both military and non-military fields. The potential of unmanned air-ground systems is immeasurable, with predictable and broad applications in hazardous work such as manufacturing, construction, agriculture, hazardous chemical spills, geological disasters, disaster relief, and traffic accidents [13; 14; 15]. To expand the scope of application scenarios and working capabilities of UGVs/UAVs, it is necessary to improve the autonomous navigation capabilities of unmanned air-ground systems in unknown working environments. Traditional path planning algorithms for the air-ground unmanned system mainly include A* algorithm [16], ant colony algorithm [17], artificial potential field method [18], genetic algorithm [19], etc. However, in many applications of air-ground unmanned systems, the complex and unpredictable operating environment requires path planning methods to exhibit self-learning, adaptability, and robustness. Similarly, traditional methods cause high computational costs in highly unstructured environments, such as the stochastic dynamic behavior of obstacles and the unknown nature of the environment [20]. To overcome the weaknesses of these methods, multiple solutions have been proposed. Reinforcement learning techniques can learn appropriate actions from environmental states, with benefits based on the concept of online learning and rewards or punishments from the environment, which allows the agent to modify its policy based on the rewards or punishments received. Deep reinforcement learning (DRL) has received much attention in recent years in artificial intelligence due to its end-to-end mapping and label-free interaction with the environment. DRL has been widely used in various fields such as video prediction, target localization, text generation, robotics, machine translation, control optimization, autonomous driving, and text-based games, showing strong adaptation and learning capabilities. Currently, DRL has been well applied to mobile robot path planning problems with important results [21]. However, when a UGV/UAV employs reinforcement learning to plan a course in an unfamiliar environment, it can only arrive at the optimal solution through trial and error. The applications of search-based path planning have been mature and capable of not only dynamic obstacle avoidance in known environments, but also path optimization for multiple aspects of path smoothing, denoising, and control. Integrating search-based algorithms into reinforcement learning can not only make full use of known information to efficiently learn to accelerate convergence, but also improve the performance of training. Therefore, based on our previous work [22], this paper proposes an end-to-end path planning framework for air-ground unmanned system navigation, search-based optimizing DRL (SO-DRL), based on the framework of deep reinforcement learning, combined with excellent search-based path planning algorithms.