As one of the commonly used vehicles for underwater detection, underwater robots are facing a series of problems. The real underwater environment is large-scale, complex, real-time and dynamic, and many unknown obstacles may exist in the underwater environment. Under such complex conditions and lack of prior knowledge, the existing path planning methods are difficult to plan, therefore they cannot effectively meet the actual demands. In response to these problems, a three-dimensional marine environment including multiple obstacles is established with the real ocean current data in this paper, which is consistent with the actual application scenarios. Then, we propose an N-step Priority Double DQN (NPDDQN) path planning algorithm, which potently realizes obstacle avoidance in the complex environment. In addition, this study proposes an experience screening mechanism, which screens the explored positive experience and improves its reuse rate, thus efficiently improving the algorithm stability in the dynamic environment. This paper verifies the better performance of reinforcement learning compared with a variety of traditional methods in three-dimensional underwater path planning. Underwater robots based on the proposed method have good autonomy and stability, which provides a new method for path planning of underwater robots. Note to Practitioners—The goal of this study is to provide a new solution for obstacle avoidance in path planning of underwater robots, which is consistent with the dynamic and real-time demands of the real environment. Existing underwater path planning researches lack a consistent environment with the actual application, and therefore we firstly construct a three-dimensional ocean environment with real ocean current data to provide support for the algorithms. Additionally, most of the algorithms are pre-planning methods or require long-time calculation, and there is little research on obstacle avoidance. In the face of obstacle changes, underwater robots with poor adaptability will cause performance decline and even economic losses. The proposed algorithm learns through interaction with the environment, and therefore it does not require any prior experience, and has good adaptability as well as fast inference speed. Especially, in the dynamic environment, algorithm performance is difficult to guarantee due to less positive experience in exploration. The proposed experience screening mechanism improves the stability of the algorithm, so that the underwater robot maintains stable performance in different dynamic environments.

Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.