Considering the wide-area distribution and limited transmission power of sensing devices in the Internet of Underwater Things (IoUT), employing autonomous underwater vehicles (AUVs) to collect data is considered a promising solution. While most existing AUV-assisted data collection schemes primarily focus on enhancing data collection throughput and identifying the shortest path, they often overlook the influence of the underwater environment on AUV and the timeliness of data collection. In this paper, we design a multi-AUV-assisted data collection system, in which AUVs select their own target devices to collect data according to the data upload urgencies of IoUT devices. Considering the disturbance of turbulent ocean environment and the limited energy of AUV, we propose an environment and energy-aware AUV-assisted data collection scheme. This scheme aims to conduct path planning for multiple AUVs based on perceived environmental information, including turbulent fields and device statuses. The primary goals are to maximize the sum data collection rate and total data throughput, minimize AUV energy consumption, reduce the average data overflow times. To solve this high-dimensional NP-hard problem, we first model the problem as a Markov decision process, and propose a multi-agent independent soft actor-critic to solve it. Extensive simulations validate the effectiveness and adaptability of our approach.

With the development of underwater technology and the increasing demand for ocean development, more and more intelligent equipment is being applied to underwater scientific missions. Specifically, autonomous underwater vehicle (AUV) clusters are being used for their flexibility and the advantages of carrying communication and detection units, often performing underwater tasks in formation. In order to locate AUVs with high precision, we introduce an unmanned surface vehicle (USV) with global positioning system (GPS) and propose a USV–AUV network. Furthermore, we propose an ultra-short baseline (USBL) acoustic cooperative location scheme with an orthogonal array, which is based on underwater communication with sonar. Based on the derivation of the Fisher information matrix formula under Cartesian parameters, we analyze the positioning accuracy of AUVs in different positions under the USBL positioning mode to derive the optimal array of the AUV formation. In addition, we propose a USV path planning scheme based on Dubins path planning functions to assist in locating the AUV formation. The simulation results verify that the proposed scheme can ensure the positioning accuracy of the AUV formation and help underwater research missions.

Unmanned underwater vehicles (UUVs) have been widely used in various ocean applications, such as underwater exploration and data collection. And the underwater pursuit-evasion game (UPEG) is the key to efficient implementation of other tasks, holding significant research value. However, testing the UPEG task in real ocean environment is both costly and risky, and currently, UUV control algorithms that rely on specific environmental models struggle to complete the complicated UPEG task. To address above challenge, we propose UPEGSim, an UUV simulator specifically designed for the UPEG task. Built through Gazebo and robot operating system, UPEGSim provides a reinforcement learning (RL) environment to train UUVs for improving the intelligent performance in the UPEG task. Furthermore, we propose an efficient UPEG training framework (ETFDU), which includes multiagent decentralized training and execution techniques, scene transfer training methods, and offline RL techniques based on decision transformer, to facilitate efficient UUV training. Through training on the UPEG task in UPEGSim, we validate the effectiveness and feasibility of the proposed UPEGSim simulator and the ETFDU training framework.

Parkinson's Disease (PD) treatment is challenging due to symptom heterogeneity and the lack of a definitive cure. Lifelong medication requires personalized treatment plans developed by physicians, but such approaches are constrained by high costs and limited physician capacity. Although deep learning (DL) methods have been explored, they lack interpretability and are restricted to numerical data inputs. In this study, we propose a novel framework that leverages large language models (LLMs) to design personalized PD treatment strategies, integrating both patient information in natural language form and external textual knowledge sources (e.g., medical guidelines). To enhance effectiveness, we use Monte Carlo Tree Search (MCTS) to refine strategies and establish a robust medication recommendation dataset. To enhance reliability and interpretability, we incorporate Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning within the LLM system, ensuring that each proposed strategy is accompanied by step-by-step explanations and references to similar historical cases. Experimental evaluations using the Parkinson's Progression Marking Initiative (PPMI) dataset show that our method surpasses physician-prescribed treatments, achieving an average reduction of over 1.4 points in the revised unified Parkinson's disease rating scale part III (MDS-UPDRS-III) scores. Our method also outperforms the RL-method by 1.01 points on average. Furthermore, over 43% of patients achieve more than 2 point-reduction of MDS-UPDRS-III scores. A detailed case study highlights the flexibility of LLMs in dynamically adjusting medication plans for patients at different disease stages, highlighting its potential to advance personalized PD management in real-world settings.

It is significant to employ multiple autonomous underwater vehicles (AUVs) to execute the underwater target tracking task collaboratively. However, it’s pretty challenging to meet various prerequisites utilizing traditional control methods. Therefore, we propose an effective two-stage learning from demonstrations training framework, FISHER, to highlight the adaptability of reinforcement learning (RL) methods in the multi-AUV underwater target tracking task, while addressing its limitations such as extensive requirements for environmental interactions and the challenges in designing reward functions. The first stage utilizes imitation learning (IL) to realize policy improvement and generate offline datasets. To be specific, we introduce multi-agent discriminator-actor-critic based on improvements of the generative adversarial IL algorithm and multi-agent IL optimization objective derived from the Nash equilibrium condition. Then in the second stage, we develop multi-agent independent generalized decision transformer, which analyzes the latent representation to match the future states of high-quality samples rather than reward function, attaining further enhanced policies capable of handling various scenarios. Besides, we propose a simulation to simulation demonstration generation procedure to facilitate the generation of expert demonstrations in underwater environments, which capitalizes on traditional control methods and can easily accomplish the domain transfer to obtain demonstrations. Extensive simulation experiments from multiple scenarios showcase that FISHER possesses strong stability, multi-task performance and capability of generalization.