The path planning of the autonomous underwater vehicle (AUV) has shown great potential in various Internet of Underwater Things (IoUT) applications. Although considerable efforts had been made, prior studies are confronted with some limitations. For one thing, existing work only uses the ocean current simulation model without introducing real ocean information, having not been supported by real data. For another, traditional path planning algorithms have strong environment dependence and lack flexibility: once the environment changes, they need to be remodeled and replanned. To overcome these challenges, this article proposes comprehensive ocean information D3QN (COID), an AUV path planning scheme exploiting comprehensive ocean information and reinforcement learning (RL), which consists of three steps. First, we introduce the comprehensive real ocean data, including weather, temperature, thermohaline, current, etc., and apply them into the regional ocean modeling system to generated reliable ocean current. Next, through well-designed state transition function and reward function, we build a 3-D grid model of ocean environment for RL. Furthermore, based on the framework of the double dueling deep $Q$ network (D3QN), COID integrates local ocean current and position features to provide state input and uses priority sampling to accelerate network convergence. The performance of COID has been evaluated and proved by numerical results, which demonstrate efficient path planning and high flexibility for expansion into different ocean environments.

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.

The unmanned aerial vehicles (UAVs) are competent to perform a variety of applications, possessing great potential and promise. The deep neural networks (DNN) technology has enabled the UAV-assisted paradigm, accelerated the construction of smart cities, and propelled the development of the Internet of Things (IoT). UAVs play an increasingly important role in various applications, such as surveillance, environmental monitoring, emergency rescue, and supplies delivery, for which a robust path-planning technique is the foundation and prerequisite. However, existing methods lack comprehensive consideration of the complicated urban environment and do not provide an overall assessment of the robustness and generalization. Meanwhile, due to the resource constraints and hardware limitations of UAVs, the complexity of deploying the network needs to be reduced. This article proposes a lightweight, reinforcement-learning-based (RL-based) real-time path-planning method for UAVs, adaptive SAC (ASAC) algorithm, which optimizing the training process, network architecture, and algorithmic models. First of all, we establish a framework of global training and local adaptation, where the structured environment model is constructed for interaction, and local dynamically varying information aids in improving generalization. Second, ASAC introduces a cross-layer connection approach that passes the original state information into the higher layers to avoid feature loss and improve learning efficiency. Finally, we propose an adaptive temperature coefficient, which flexibly adjusts the exploration probability of UAVs with the training phase and experience data accumulation. In addition, a series of comparison experiments have been conducted in conjunction with practical application requirements, and the results have fully proved the favorable superiority of ASAC.

Autonomous underwater vehicle (AUV) shows great potential in the Internet of Underwater Things (IoUT) system, in which the path planning algorithm plays a fundamental role. However, the complex underwater environment brings greater challenges to AUV path planning, especially the ocean current, which has a profound impact on time and energy consumption. This article focuses on the complex ocean current condition and proposes an underwater path planning method based on proximal policy optimization (UP4O). In this novel method, a deep reinforcement network is constructed to serve as a decision control to plan the moving direction of AUV. An information encoding module is developed to extract the features of the local obstacles. Furthermore, UP4O integrates the obstacle features with the current state information, including relative position, ocean current, and velocity, enabling the AUV to focus on the global direction and local obstacles at the same time. Additionally, to further adapt to the ocean current and shorten the time cost, UP4O expands the action space of AUV, realizing a fine and flexible action adjustment. The wide applicability of UP4O has been proved by numerous experiments. The proposed algorithm can always plan the time-saving and collision-free paths in complex underwater environments with various terrains and ocean current.

With the rapid advancement of modern society, autonomous systems have been broadly applied in people’s daily lives. Under the guidance of this trend, autonomous vehicles have gradually become popular. However, due to some adverse factors(such as insufficient computing force and limited communication bandwidth) in edge computing scenarios and the lack of autonomous decision-making ability, the safety of autonomous vehicles is not enough. It is a good solution to use a deep reinforcement learning(DRL) algorithm, which combines deep learning(DL) and reinforcement learning(RL), to provide a fast convergence speed and an appropriate decision-making ability. In this paper, based on Soft-Actor-Critic(SAC) and Soft-Actor-Critic-Discrete(SAC-D), we propose a Double Bootstrapped Soft-Actor-Critic-Discrete(DBSAC-D) algorithm. By introducing bootstrap, the ability to explore in action space is enhanced, the value of each action is accurately judged, the convergence process is accelerated and the consumption of the computing force is reduced. In addition, we also propose a novel sampling strategy, which balances the novelty and importance of the sampled data, and improves the training value of the sampled data to the network model. The experimental results show that our proposed algorithm achieves good performances in several traffic scenes and has a fast convergence speed.