## 1 Introduction
A lot of environmental variables cannot be acquired in advance, including precise position information, so it is research of great practical significance to enable the UAV to make decisions on-the-fly and to learn the optimal flight strategy under the condition that information about the surroundings is not known [17]. Therefore, learning the optimal flight strategy for UAVs to make instant decisions under unknown surrounding environment information is a research direction of practical significance. In ref. [18], the authors investigate the jointly optimal trajectory and time switching allocation design for UAV-secure communication systems with imperfect eavesdropper locations. However, most of the existing UAV data collection schemes are based on the assumption that the position information of SNs is known and fixed and can be provided to UAVs for use. This assumption is valid in some specific scenarios where SNs are fixedly installed at specific locations. In many practical applications, this assumption does not conform to the actual situation. SNs may be randomly deployed in an area and may change their positions due to various reasons, such as wind force, animal interference, human intervention, and so on. In this case, UAVs cannot obtain the precise position information of IoT devices but only the information of the region where they are located. Therefore, how to design an effective UAV-path-planning scheme to minimize the completion time of data collection tasks without knowing the exact position information of SNs becomes a challenging and practical problem. Reinforcement Learning (RL) is a kind of unsupervised learning, which aims at obtaining an environment model through adaptive learning and adjusting its parameters in order to obtain the optimal action strategy when facing time-varying environments [19]. Deep Reinforcement Learning (DRL) breaks through the limitations of complex environments, and the training speed of neural networks is significantly improved, which has led to its use in a large number of applications. Therefore, DRL has been widely used in complex communication problems in wireless channels. The integration of DRL shows a good application prospect in communication, but there are still some deficiencies in practice. To solve these problems, DeepMind proposes Deep Q-Learning (DQN), which can realize an end-to-end learning policy without increasing empirical knowledge. DQN has played an essential role in communications [20]. In ref. [21], [PERSON] et al., propose a path-planning and spectrum-allocation method for UAVs based on DRL to realize efficient and energy-saving, fair communication. According to all of the above, this paper proposes a DRL approach to minimize the total mission time for UAV-assisted data collection. To resolve the lack of location information, we leverage DQN for trajectory design to minimize the duration of mission completion without precise device location information being known. The main contributions of this paper are as follows:  
* The goal of this paper is to optimize data collection in photovoltaic systems by the minimizing mission completion time of a UAV. Unlike the existing works, we consider this issue in situations where the UAV cannot obtain the precise location information of SNs in a power system. First, a UAV data acquisition system model is established under the condition that the location information of SNs is unknown. The UAV departs from the base station and collects the information sensed by all SNs during its flight time. Due to the lack of positional information, the system senses the changes in signal strength from ground devices to infer the relative positions of the UAV and the ground devices, and then enables the UAV to select a better flight trajectory. * To make up for the lack of location information, we formulate this problem as a Markov Decision Process (MDP), which proposes a DQN-based algorithm to minimize the flight time under a lack of location information.