Unmanned underwater vehicles (UUVs) are becoming increasingly important for a variety of applications, including ocean exploration, mine detection, and military surveillance. This paper aims to provide a comprehensive examination of the technologies that enable the operation of UUVs. We begin by introducing various types of unmanned vehicles capable of functioning in diverse environments. Subsequently, we delve into the underlying technologies necessary for unmanned vehicles operating in underwater environments. These technologies encompass communication, propulsion, dive systems, control systems, sensing, localization, energy resources, and supply. We also address general technical approaches and research contributions within this domain. Furthermore, we present a comprehensive overview of related work, survey methodologies employed, research inquiries, statistical trends, relevant keywords, and supporting articles that substantiate both broad and specific assertions. Expanding on this, we provide a detailed and coherent explanation of the operational framework of UUVs and their corresponding supporting technologies, with an emphasis on technical descriptions. We then evaluate the existing gaps in the performance of supporting technologies and explore the recent challenges associated with implementing the Thorp model for the distribution of shared resources, specifically in communication and energy domains. We also address the joint design of operations involving unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and UUVs, which necessitate collaborative research endeavors to accomplish mission objectives. This analysis highlights the need for future research efforts in these areas. Finally, we outline several critical research questions that warrant exploration in future studies.

The increasing demand for adaptive and autonomous smart ocean systems has accelerated the deployment of Autonomous Underwater Vehicles (AUVs) in various complex underwater missions, including collaborative navigation, target tracking, data collection, and energy management. To address the inherent challenges of the underwater environment, such as the limitations of acoustic communication, unpredictable environmental dynamics, and partial observability, Multi-Agent Reinforcement Learning (MARL) with the Centralized Training with Decentralized Execution (CTDE) paradigm has emerged as a promising solution. This paper presents a focused survey on MARL algorithms and their applications in multi-AUV systems. We classify the algorithms into value-based approaches (e.g., VDN and QMIX), policy-based approaches (e.g., Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Proximal Policy Optimization (MAPPO)), and hybrid approaches that support explicit communication (e.g., CommNet, DIAL, and ROMA). We further review several relevant simulation environments, such as PettingZoo, UWSim, and Gazebo, along with underwater acoustic channel modeling and key performance metrics used for system evaluation. Complementing the survey study, we present representative experiments in data collection and energy efficiency scenarios using MADDPG and MAPPO algorithms. The results include comparative analyses of average reward, overflow, Flight eXceedance (FX), and energy consumption, accompanied by trajectory visualizations in a grid-based simulation environment. Finally, we provide a synthesis of future research directions, including the integration of Meta-Reinforcement Learning (Meta-RL), graph-based role allocation, and 3D physics-based simulator training, to strengthen the adoption of MARL in autonomous ocean systems. This survey is expected to serve as a reference point for further research in developing AUVs that are smarter, more collaborative, and resilient to extreme conditions.

As the demand for adaptive and autonomous smart ocean systems continues to grow, multi-agent control strategies based on reinforcement learning for Autonomous Underwater Vehicles (AUVs) play a vital role in supporting data collection in challenging deep-sea environments. Unlike previous surveys, this paper presents a comprehensive review of Multi-Agent Reinforcement Learning (MARL) approaches with a specific emphasis on energy efficiency and inter-AUV coordination.We examine various MARL algorithms and their applications in real-world scenarios such as buffer overflow prevention, avoidance of Flight eXceedance (FX) violations, and adaptive path planning. As a supporting illustration, we include a case study based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to demonstrate how coordinated policies can be formed under energy-constrained and partially observable scenarios. Additional experiments using MAPPO and MODDPG show that MODDPG excels in energy efficiency with low overflow, while MAPPO yields moderate rewards but lacks training stability. These results provide a conceptual foundation for validating energy-efficient reward strategies based on decentralized coordination. We also acknowledge that the simulation used is still limited to an idealistic 2D grid environment and does not fully represent real ocean dynamics. Therefore, we plan to extend it to a 3D particle-based or Computational Fluid Dynamics (CFD) framework, along with integration of ocean environmental data from historical sources such as NOAA, JAMSTEC, and Copernicus Marine. Major challenges such as non-stationarity in agent interactions, limitations of acoustic communication, and the simulation-to-reality gap are also discussed. Future research directions include Meta-Reinforcement Learning (Meta-RL), adaptive role assignment based on energy utility, large-scale decentralized MARL architectures, and training based on realistic ocean scenarios. This review and the supporting experiments are expected to serve as a strategic foundation for the development of efficient, robust, and scalable multi-agent AUV systems for future marine missions.

Perkembangan teknologi Augmented Reality (AR) merubah batas dan memperluas dunia visual kita, AR memiliki kelebihan bersifat interaktif dan real time sebagai media komunikasi visual. Disisi lain pemanfaatan teknologi ini mulai jamak digunakan di berbagai bidang, pada penelitian kali ini peneliti mencoba mengaplikasikan teknologi ini untuk tujuan memperkenalkan obyek wisata bersejarah Lawang Sewu di Kota Semarang diangkat sebagai obyek visual yang akan ditampilkan menggunakan teknologi AR. Memperkenalkan obyek visual menggunakan teknologi AR sendiri bukan hal yang baru, sudah banyak dilakukan di luar negeri, misal di China oleh Yi Ji, et al dari Guangdong University of Technologi Guangzhou, China, (2019). Yang menggunakan teknologi Augmented Reality Berbasis Web (WebAR) untuk mengenalkan kerajinan tangan tradisional (handycraft) dan cara pembuatannya, sebagai materi visualnya. Teknologi AR berbasis web dipilih, mengingat teknologi ini relatif baru, mudah diaplikasikan dan dapat berjalan lintas platform dan yang terakhir karena alasan perkembangan teknologi komunikasi, khususnya 5G didunia, dan peneliti meyakini teknologi ini akan menjadi tren kedepannya. Hasil penelitian menunjukkan aplikasi virtual tour menggunakan AR berbasis web ini mudah dikembangkan, sedangkan dari hasil pengujian menunjukkan aplikasi dapat berjalan dengan baik dengan metode pemindaian vertikal maupun horisontal, dan hasil pengujian menggunakan usability test dengan melibatkan 27 responden menunjukkan sebanyak 91,53% responden setuju bahwa aplikasi mudah digunakan, 94,18% responden setuju bahwa informasi visual dapat dilihat dengan jelas, 93,65% reponden berpendapat bahwa informasi visual yang ditampilkan mudah diingat 96,83 responden menikmati menggunakan aplikasi untuk mempelajari obyek bangunan bersejarah lawang sewu dan 96,83% responden tertarik untuk untuk menggunakan aplikasi ini kembali nantinya untuk obyek virtual yang lain. Sehingga peneliti menyimpulkan bahwa aplikasi virtual tour bisa dikembangkan menggunakan AR berbasis web dan menyarankan penggunaannya secara luas untuk virtual tour obyek visual yang lain.

The development of the world of robotics is inevitable with the rapid development of supporting science and technology. There are various types and classifications of robots, although the basic development is not much different. One type of robot that is in demand and the most widely developed is the wheeled robot. The robot component itself is generally divided into 3 parts, the first sensor, the second processor or component processor and actuator, in this study which behaves as an actuator is a wheel, while that behaves as a sensor, researchers utilize brainwave reader headsets from neurosky, and those that served as a processor component or processor using Arduino Uno R3. The neurosky headset works wirelessly using a Bluetooth connection, and the data sent is in the form of a brain wave power level (blink streght level). Before it can be translated into a telepathic brain command, this signal is first captured and processed using an android handset using an application that is built based on blynk IoT, then after that the command is sent to Arduino as a robot processing component that has previously been fitted with HC-06 bluetooth module hardware. to capture wireless broadcasts from an android device, only after that the signal is processed by Arduino becomes a command to move forward, backward, left, right wheeled robot by the L298N motor driver. The test results in an ideal environment showed an average system success of 85%, while testing in a non-ideal environment (with obstacles of space and distance) showed an average system success of 40% with each test carried out 10 times.

This study aims to determine the numeracy literacy skills of introverted and extroverted students in solving linear programming problems. The method used in this research is descriptive qualitative. The subjects taken were two students of class XI MIPA-2 MA Banat Tajul Ulum Brabo with details of one introvert student and one extrovert student. Data collection techniques were carried out using questionnaires, written tests, and interviews. Retrieval of research data through written tests and interviews. Based on the results of this study, it shows that introverted subjects fulfill all indicators of numeracy literacy skills, namely solving problems using various numbers and symbols related to mathematics; analyze the information presented in various descriptions (graphs, tables, charts, diagrams and so on); and trap the results of that analysis to make predictions and decisions. Meanwhile, the extrovert subject did not fulfill one of the three indicators of numeracy literacy, namely the confidentiality of the results of the analysis to make predictions and decisions.