## 1 Introduction  
UGVs can replace humans in performing hazardous tasks with their superior speed, mobility, and atmospheric independence [1]. In recent years, research into UGV control has concentrated on navigation, SAR, area coverage, and military tasks. As an important function of UGV, SAR has received significant attention in academia, with the key focus areas including search area prediction [2; 3], SAR force configuration [4], and rescue path planning [5; 6; 7]. The complex structure of mining caves lacks environmental information, and there is a risk of secondary explosions and collapses during the rescue process. To address the above dilemma, UGVs (see more detailed information about mining UGVs in [8]) have been considered to replace humans in dangerous SAR missions due to their advantages of low cost and high efficiency. Additionally, their path planning is a prerequisite for subsequent rescue missions. Therefore, the path-planning problem derived from this has always been an important topic in the field of UGV applications [9; 10; 11]. Previously, the research focus of SAR was on point-to-point path planning. According to the difference in target information, it can be categorized as known and unknown target path planning [12]. When in different application backgrounds, it can be classified as indoor planning, outdoor planning, or maritime planning [13]. In addition, according to the types of application platforms, robots can be classified as isomorphic path planners or heterogeneous path planners [14; 15; 16]. What is noteworthy is that most of the above methods plan paths based on precise or fuzzy target location information, which has high dependence on the in-variance of the demand. However, path planning without the targets' location information is more in line with the general scenario and more challenging. To address the dimension explosion caused by large amounts of data, most online POMDP methods are based on forward-looking searches, and these technologies reduce the complexity from different angles [17; 18]. Among them, the most classic is PRIMAL [19]. These methods can usually be classified into three categories: Branch-and-Bound Pruning [20; 21], Monte Carlo Sampling [22; 23; 24; 25; 26], and Heuristic Search [27; 28; 29; 30]. However, most research using these methods has the following main shortcomings: (1) agents require much training before completing path-planning tasks, namely offline learning, and spend many resources on that; (2) it is also difficult for agents to respond when task maps differ significantly from training maps. When the environmental structure is unfixed, observations are discrete through the sensor system's data acquisition process. In order to study path-planning methods, modeling the environmental information of mining caves is necessary. In addition, obviously, a path-planning problem with real-time reactions to environmental uncertainty is NP-hard, which has been proven by [PERSON] and [PERSON] using informative path planning [31]. And the partial observable Markov decision process (POMDP) search balances short-term tracking performance and the long-term final cost [32]. The environmental information objectively exists in a stable state of change, in which the subject generally lacks awareness. The external world is a generally steady resource, and stable information is considered to not require storage [33]. Targeting the challenges above, this paper proposes a gray Q-Learning (GQL) search area path-planning method using UGV as the agent. This method combines the even gray model (EGM) prediction model, the MAI gray decision process, and the Q-Learning (QL) action planning model. The main contributions of this paper are as follows:  
* A method for constructing an environmental model and an information feature mining method based on EGM are proposed to address the lack of environmental information in mining SAR. * An agent-centered path-planning model based on the RL theory is proposed for the online path-planning problem.