Sairoel Amertet

The automation of agricultural tasks using mobile robots is becoming increasingly important for precision farming. Navigation in complex, dynamic, and unstructured environments, such as fields with uneven terrain, dense vegetation, and obstacles such as rocks or irrigation systems, presents a significant challenge. Therefore, efficient path planning and collision avoidance algorithms are of particular significance. The primary objective of this study is to implement and evaluate the RRT* algorithm for coordinated path planning. A novel contribution lies in improving the planning efficiency and path optimality compared to the standard RRT algorithm, particularly through enhanced generation of time-optimal trajectories and robust collision avoidance in complex terrain. The RRT*-based collision avoidance system was evaluated through MATLAB simulations, testing its performance in scenarios with a high obstacle density typical of agricultural environments. The simulation results demonstrated a 94% success rate for trajectory planning and collision avoidance, indicating high performance potential in complex agricultural landscapes. RRT* was shown to be a highly effective trajectory planning solution for multi-wheeled agricultural robots, outperforming standard RRT. It successfully delivers optimized, collision-free trajectories in unstructured environments, offering a robust foundation for autonomous navigation. The 94% success rate obtained in the simulation validates its potential and indicates the need for further research and field testing.

The article presents a transformative learning system for predictive modeling of the dynamics of wheeled mobile robots. It overcomes the limitations of traditional static models. The proposed approach enables continuous model self-improvement by integrating physics-based simulations with data-driven adaptation, including multi-scale contextual adaptation across multiple time horizons. A comprehensive evaluation demonstrated the superiority of the proposed system over benchmark machine learning methods. The model achieved exceptional prediction accuracy (R² = 0.999) for key dynamic states, and the controller demonstrated an optimal transient response with a rise time of 0.35 s, settling time of 0.9 s, overshoot of 3 %, and error of 0.04 m. The results confirm the effectiveness of the approach in accounting for the complex dynamics of wheeled mobile robots and its potential for the development of next-generation adaptive robotic systems capable of continuously improving performance in unstructured environments.

The integration of vehicle-to-grid (V2G), grid-to-vehicle (G2V), and vehicle-to-everything (V2X) systems into an energy ecosystem represents a transformative approach. These systems enable bidirectional energy flow between electric vehicles (EVs), power grids, and other entities. In this study, the energy sources for the V2G, G2V, and V2X systems were derived from green and blue energies, emphasizing sustainability. The primary objective of this research is to optimize V2G, G2V, and V2X systems, focusing on enhancing their performance. The novel contribution of this work lies in the application of advanced optimization techniques, specifically Artificial Bee Colony Optimization (ABCO), to improve system efficiency and stability. The system was simulated in MATLAB, where ABCO achieved a 64.5% improvement in reactive power optimization over Brain Emotional Intelligent Control (BEIC). This result underscores the effectiveness of ABCO in optimizing energy exchange within the V2G, G2V, and V2X systems, confirming its suitability for these applications. These findings highlight the potential of ABCO to enhance the performance of V2G, G2V, and V2X systems, contributing to a more sustainable, resilient, and efficient energy ecosystem.

The primary challenge is to design feedback controls that enable robots to autonomously reach predetermined destinations while avoiding collisions with obstacles and other robots. Various control algorithms, such as the control barrier function-based quadratic programming (CBF-QP) controller, address collision avoidance problems. Control barrier functions (CBFs) ensure forward invariance, which is critical for guaranteeing safety in robotic collision avoidance within agricultural fields. The goal of this study is to enhance the safety and mitigation of potential collisions in smart agriculture systems. The entire system was simulated in the MATLAB/Simulink environment, and the results demonstrated a 93% improvement in steady-state error over rapidly exploring random tree (RRT). These findings indicate that the proposed controller is highly effective for collision avoidance in smart agricultural systems.

Utilizing wheeled mobile robot systems may be essential to solving some of agriculture’s upcoming problems. The present state of wheeled mobile robot systems necessitates the development of an adequate controller algorithm due to their instability, which calls for a control mechanism to enhance stability. As such, much study is needed to address this issue. Currently, proportional, integral, derivative (PID) controllers are widely employed for this purpose; however, because of parameter variations or disturbances, the PID approach is often not acceptable. Some of the problems with PID controllers can be solved with alternative control strategies, such as linear-quadratic regulator (LQR) control. For this work, a four-wheel skid-steering mobile robot’s kinematic model was created in order to evaluate the performance of LQR control. Three scenarios—only non-zero state expensive; control expensive, non-zero state cheap; and control cheap, non-zero state expensive—were analyzed using the capabilities of the wheeled mobile robot. Based on these circumstances, the peak time, settling time, and rising time for cheap control were determined to be 0.1, 7.82, and 4.39 s, respectively.

In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used to identify crop diseases, as has crop classification-based image processing, they have failed to forecast accurate crop nutrients for various crops, as crop nutrients are numerical instead of visual. Neural networks represent an opportunity for the precision agriculture sector to more accurately forecast crop nutrition. Recent technological advancements in neural networks have begun to provide greater precision, with an array of opportunities in pattern recognition. Neural networks represent an opportunity to effectively solve numerical data problems. The aim of the current study is to estimate the right crop nutrients for the right crops based on the data collected using an artificial neural network. The crop data were collected from the MNIST dataset. To forecast the precise nutrients for the crops, ANN models were developed. The entire system was simulated in a MATLAB environment. The obtained results for forecasting accurate nutrients were 99.997%, 99.996%, and 99.997% for validation, training, and testing, respectively. Therefore, the proposed algorithm is suitable for forecasting accurate crop nutrients for the crops.

Wheeled mobile robot dynamics and suitable controller design are challenging but rewarding fields of study. By understanding the dynamics of wheeled mobile robots, it could be possible to design suitable hybrid control schemes for wheeled mobile robots. Since hybrid control schemes involve combining individual control methods to create a more effective overall control strategy. This can be done in a variety of ways, such as using fuzzy linear quadratic Gaussian control. We were therefore inspired to create dynamic models and their controller designs for the wheeled mobile robot by examining the dynamics of the wheeled mobile robot. The novelty of the current paper is to hybridize the different control schemes for wheeled mobile robots in order to get better performance. Entire systems models were simulated in a MATLAB/SIMULINK environment. The results obtained for the settling time response by FLQG were 87.1% over LQG; this study compared the effectiveness of current and previous FLQG controllers in external disturbances and found peak amplitude improvements of 71.25%. Therefore, the proposed controller is suitable for use with the wheeled mobile robot.

The main problem confronting the world is human-caused climate change, which is intrinsically linked to the need for energy both now and in the future. Renewable (green) energy has been proposed as a future solution, and many renewable energy technologies have been developed for different purposes. However, progress toward net zero carbon emissions by 2050 and the role of renewable energy in 2050 are not well known. This paper reviews different renewable energy technologies developed by different researchers and their potential and challenges to date, and it derives lessons for world and especially African policymakers. According to recent research results, the mean global capabilities for solar, wind, biogas, geothermal, hydrogen, and ocean power are 325 W, 900 W, 300 W, 434 W, 150 W, and 2.75 MWh, respectively, and their capacities for generating electricity are 1.5 KWh, 1182.5 KWh, 1.7 KWh, 1.5 KWh, 1.55 KWh, and 3.6 MWh, respectively. Securing global energy leads to strong hope for meeting the Sustainable Development Goals (SDGs), such as those for hunger, health, education, gender equality, climate change, and sustainable development. Therefore, renewable energy can be a considerable contributor to future fuels.

Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems.