Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.

Autonomous vehicles (AVs) hold the potential to revolutionize transportation by improving safety, operational efficiency, and environmental impact. However, ensuring reliability and safety in real-world conditions remains a major challenge. Based on an in-depth examination of 33 peer-reviewed studies (2015–2025), this systematic review organizes advancements across five key domains: fault detection and diagnosis (FDD), collision avoidance and decision making, system reliability and resilience, validation and verification (V&V), and safety evaluation. It integrates both hardware- and software-level perspectives, with a focus on emerging techniques such as Bayesian behavior prediction, uncertainty-aware control, and set-based fault detection to enhance operational robustness. Despite these advances, this review identifies persistent challenges, including limited cross-layer fault modeling, lack of formal verification for learning-based components, and the scarcity of scenario-driven validation datasets. To address these gaps, this paper proposes future directions such as verifiable machine learning, unified fault propagation models, digital twin-based reliability frameworks, and cyber-physical threat modeling. This review offers a comprehensive reference for developing certifiable, context-aware, and fail-operational autonomous driving systems, contributing to the broader goal of ensuring safe and trustworthy AV deployment.

Currently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system by using the genetic algorithm (GA) to support the vehicle in following the predefined path accurately, minimizing the sideslip, and stabilizing the vehicle’s yaw rate. The dynamic system model of the vehicle is represented based on yaw rate angle, lateral speed, and vehicle sideslip angle as the variables of the state space model, with the steering angle as an input parameter. Using the GA to optimize the LQR control by tuning the weighting of the Q and R matrices led to enhancing the system response and minimizing deviation errors via a proposed cost function of GA. The simulation results were obtained using MATLAB/Simulink 2024a, with a representation of a predefined path as a Gaussian path. Under external and internal disturbances, such as road conditions, lateral wind, and actuator delay, the model demonstrates improved tracking performance and reduced sideslip angle and lateral acceleration by adjusting the longitudinal vehicle speed. This work highlights the effectiveness of robust control in addressing path planning, driving stability, and safety in autonomous vehicle systems.

Unmanned aerial vehicles are famous for their wide range of applications. In D3 (Dirty-Dull-Dangerous) UAV applications flight conditions may vary on large scale. External disturbances like atmospheric turbulences and gusts may be subjected to UAV, and as a result, UAV flight mission might be conducted with high level of the degradation of the accuracy. Sensor noises are also present, and theirs negligence might lead to improper dynamic performances of the closed loop control systems. Uncertainties of the control systems being structured or unstructured may tend the closed loop control system to stability bounds. In worst case, uncertainties may destabilize closed loop control systems. The purpose of the author is to present a robust controller design method called H2-optimal design ensuring stability of the closed loop control systems with simultaneous dynamic performances predefined for the closed loop control system.

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero.

Unmanned aerial vehicles are widely spread and intensively used ones both in governmental and in private applications. The standard arrangements of the commercial-off-the-shelves unmanned aerial vehicles sometimes neglect application of the automatic flight control system onboard. However, there are many initiatives to ensure autonomous flights of the unmanned aerial vehicles via pre-programmed flight paths. Moreover, automatic flight control system can ensure necessary level of the flight safety both in VFR and IFR flights. The aim of this study is to guide UAV users in set up commercial onboard autopilots available on the market. On the contrary, fitness of the autopilot to a given type of the air robot is not guaranteed, and, an extra load on users can appear in controller settings. The proposed pole placement technique is one of the proper methods eliminating difficulties, and, computer aided gain selection using MATLAB will be presented.