## 1 Introduction  
The interest in and demand for unmanned vehicles that perform unmanned missions have continuously increased; in particular, UAV (unmanned aerial vehicle)-related technologies are being developed rapidly. However, UAVs have limitations such as short operating time and small payload, making it difficult to mount various equipment and to acquire data near the ground. If UGVs (unmanned ground vehicles) perform missions together, more diverse missions can be performed more efficiently. Such research is actively underway worldwide. [PERSON] [1] tried to create a UGV route more precisely using UAV images, while [PERSON] [2] developed a system for local search simultaneously using both UAVs and UGVs. In addition, in order to overcome the short operation time of UAVs, [PERSON] [3] studied an operation method considering charging, while [PERSON] [4] studied how to optimize the UAV's path for charging. Since this study is one in which one operator simultaneously operates multiple UAVs and UGVs, the operator cannot be in the field where the UGVs and UAVs operate. Therefore, the UGVs are controlled remotely, and an autonomous driving function must be implemented because one person cannot directly control multiple unmanned vehicles at the same time. Autonomous driving is being actively researched by numerous companies, universities, and research institutes around the world. Overcoming the uncertainty encountered while driving is the key, and, for this purpose, autonomous driving consists of environmental awareness, path planning, and driving control [5]. Environment recognition is a technology that recognizes the surrounding environment for autonomous driving, and cameras and LiDAR are often used as sensors. Path-planning methods are classified into graph-search-based, sampling-based, interpolation-based, and optimization-based methods [5], whereby a path that can be safely driven is created using environmental awareness information. Graph-search-based methods find a path by creating a number of nodes within the domain and composing it as a graph [6], including methods such as the Dijkstra [7] algorithm that finds the shortest path and the A-Star [8] algorithm that finds the optimal path using a heuristic method. Sampling-based methods randomly sample and connect to the surrounding space [9], including methods such as the probabilistic roadmap method [10] and rapidly explored random tree [11]. Interpolation-based methods define a function with multiple coefficients and find the coefficients [12]. Optimization-based methods formulate an optimization problem consisting of constraints and cost functions, thereby finding a path that minimizes the cost function, including methods such as the potential field algorithm [7; 8] and model predictive control (e.g., [13]). The potential field method is broadly applied for real-time collision-free path planning [14]. In this method, an attractive force is generated in the goal direction, and a repulsive force is generated from an obstacle. Although the potential field algorithm is often used because it is mathematically simple, problems such as a local minimum and vibration around obstacles occur [14]. [PERSON] [15] proposed new formulas for reducing oscillations and avoiding conflicts when obstacles are located near a target. [PERSON] [16; 17] proposed a new framework to escape from the local minimum of a robot path using the potential field method. Many studies have tried to apply path-planning algorithms in real time [18; 19]; although the potential field method is relatively efficient, it still requires substantial effort to use it in real time. In this study, an environmental recognition and control system for autonomous driving was configured in a UGV driven based on a GPS target point to perform a ground-aerial cooperative mission, and local path planning was performed by applying the potential field algorithm as an optimization-based method.