This paper proposes a novel adaptive intelligent global sliding mode control for the tracking control of a DC-DC buck converter with time-varying uncertainties/disturbances. The proposed control law is formulated using a switching surface that eliminates the reaching phase and ensures the existence of the sliding action from the start. The control law is derived based on the Lyapunov stability theory. The effectiveness of the proposed approach is illustrated via high-fidelity simulations by means of Simscape simulation environment in MATLAB. Satisfactory tracking accuracy, efficient suppression of the chattering phenomenon in the control input, and high robustness against uncertainties/disturbances are among the attributes of the proposed control approach.

In the past few years, due to the growth of the open-source community and the popularity of perceptual computing resources, the ROS (Robotic Operating System)Ecosystem has been widely shared and used in academia, industrial applications, and service fields. With the advantages of reusability of algorithms and system modularity, service robot applications are flourishing via the released ROS navigation framework. In the ROS navigation framework, the grid cost maps are majorly designed for path planning and obstacle avoidance with range sensors. However, the robot will often collide with dynamic obstacles since the velocity information is not considered within the navigation framework in time. This study aims to improve the feasibility of high-speed dynamic obstacle avoidance for an ROS-based mobile robot. In order to enable the robot to detect and estimate dynamic obstacles from a first-person perspective, vision tracking and a laser ranger with an Extend Kalman Filter (EKF) have been applied. In addition, an innovative velocity obstacle layer with truncated distance is implemented for the path planner to analyze the performances between the simulated and actual avoidance behavior. Finally, via the velocity obstacle layer, as the robot faces the high-speed obstacle, safe navigation can be achieved.

The convergence of Unmanned Aerial Vehicles (UAVs) and Federated Learning (FL) is creating a new paradigm for decentralized intelligence. In this setup, UAVs are not merely new clients; their unique capabilities introduce fundamentally new structural roles that change how FL systems are designed and operate. This systematic literature review addresses this evolving landscape by answering three key questions: 1) How do UAVs reshape FL architectures and roles? 2) In which key domains does combining UAVs with FL solve critical problems? 3) What challenges and mitigation strategies are reported for UAV–FL integration? To answer these questions, we synthesized findings from 272 peer-reviewed journal articles published through late 2025. The review offers a practitioner-oriented guide. First, it provides an architectural blueprint based on the different roles of UAVs, allowing for effective system design. It then offers a playbook of real-world applications, helping build a strong business case for new projects by highlighting promising domains (e.g., satellite-backed maritime communications and wearable health analytics), and revealing gaps and underexplored opportunities. To ensure robust deployment, we outline key implementation challenges and provide actionable guide to mitigate each specific challenge. Finally, we highlight the key gaps that remain and propose focused directions for future work. A keyword co-occurrence map is also provided as a supporting visualization, showing how topics cluster and where potential integration opportunities may lie.

The state of the art of mobile robot path planning is composed with global planner and local planner. For example, the global planner majorly establishes a suitable path from a knowing map based on the shortest path. And the local planner which computes the velocity command that includes obstacle clearance and progress toward the goal. However, due to the 2D laser ranger on a mobile service robot just detects the part of the meal table or misses it, this will cause robot often hits the meal table. In this work we apply adaptive control method to feed the goals for Dynamic Window Approach’s (DWA) computing. The experimental result shows that DWA will follow the pre-defined path closely and smoothly.