Robots have made significant contributions across various industries due to their efficiency and effectiveness. However, indoor navigation remains challenging due to complex environments and sensor signal interference. Changes in indoor conditions and the limited range of GPS signals necessitate the development of an accurate and efficient indoor robot navigation system. This study aims to create an autonomous indoor navigation system for medical robots using sensors such as Marvelmind, LiDAR, IMU, and an odometer, along with the Time Elastic Band (TEB) local planning algorithm to detect dynamic obstacles. The algorithm’s performance is evaluated using metrics like path length, duration, speed smoothness, path smoothness, Mean Squared Error (MSE), and positional error. In the test arena, TEB demonstrated superior efficiency with a path length of 155.55 m, 9.83 m shorter than the Dynamic Window Approach (DWA), which covered 165.38 m, and had a lower yaw error of 0.012 radians. TEB outperformed DWA in terms of speed smoothness, path smoothness, and MSE. In the Sterile Room Arena, TEB had an average path length of 14.84 m, slightly longer than DWA’s 14.32 m, but TEB navigated 2.82 s faster. Additionally, TEB showed better speed and path smoothness. In the Obstacle Room Arena, TEB recorded an average path length of 21.96 m in 57.3 s, outperforming DWA, which covered 23.44 m in 61 s, with better results in MSE, speed smoothness, and path smoothness, highlighting superior path consistency. These findings indicate that the TEB algorithm is an effective choice as a local planner in dynamic hospital environments.

The sustainable development goals (SDGs) are a UN agenda that has been approved by all UN member states. The SDGs have 17 targets, one of which is to eliminate hunger. In 2050, the world’s population is expected to reach 9.7 billion people. Improved soil and water management, according to the World Resources Institute, is one of the options for feeding 10 billion people sustainably by 2050. In comparison to conventional farming, smart and precision farming produces higher productivity at a lower cost. Based on the search for literature studies related to the development of agricultural technology, it was found that communication methods and online interfaces still require further improvement. The steps for developing the system are designing the architecture and end-to-end communication flow, designing use case diagrams, designing entity-relationship diagrams, designing user flow diagrams, implementing the system through code development, and finally testing the system. Planned communication and web design for precision smart agriculture are implemented effectively. The MQTT is used to communicate with the Node.js server worker. Data from numeric image feeds and images are directly processed by the system. The server will store all received data, including numeric data and live feeds, for future use. The back end of the website has many functions such as dataset management, device management, user administration, firmware management, control management, and live image feed management are some of the capabilities available. When 100 users access the system simultaneously, the RAM usage on the server is 167 MB. RAM utilization reaches 389 MB when 400 users access the system simultaneously. The limit for simultaneous user connections to the web interface is 400 users. The maximum number of devices that can be connected simultaneously via MQTT communication is 900.

This investigation seeks to discern the aerodynamic differentials between rockets featuring straight fins and those with curved fins, focusing on the drag coefficient (cd), lift coefficient (cl), and moment coefficient (cm). Employing the computational fluid dynamics (CFD) methodology within ANSYS Fluent software, the research endeavors to provide comprehensive insights. The rockets under scrutiny share a common cylindrical body configuration, boasting a 70 mm diameter, a conical nose, and four symmetrically positioned fins along the lower body. The CFD analyses encompass subsonic Mach 0.6 and supersonic Mach 1.2 scenarios, with the angle of attack systematically varying from 0° to 25° at 5° intervals for each velocity setting. The outcomes of the simulations reveal notable trends: both cd and cl exhibit an upward trajectory, while cm experiences a decrement with escalating angles of attack and velocities. The culmination of Ansys CFD simulations for both rocket configurations unequivocally indicates superior flight performance for the straight fin rocket. This discernment is grounded in the observed amplification of drag and lift coefficients, coupled with the concomitant reduction in the moment coefficient, thus elucidating the nuanced aerodynamic distinctions between straight fin and curved fin rockets across varying flight conditions.

The increasing need for agricultural commodities poses a serious threat to the cultivating ability of agricultural supplies. The use of autonomous farming drones to support the cultivation process is a rapidly growing trend. In an effort to improve efficiency and accuracy in watering using drones, this research strived to propose a control design for the drones using the multiclass support vector machine (MSVM) method. The proposed system was an improvement compared to the common approach of using constant watering strength in all drone conditions. By obtaining suitable watering strength based on the drone’s altitude, wind speed, and speed sensor data as the input, the optimal solution between water usage and water efficiency was expected to be achieved. An experimental trial that consisted of 12 flights was conducted to acquire a data set with 3,750 data. The results of classification with MSVM obtained an accuracy of 90.82%. The efficiency of using the water resource and the accuracy of delivering the correct amount of water based on the drone condition were achieved. These results show that the proposed technology has great potential for using drones as an automatic watering system in agriculture.