This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms.

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary.

This article focuses on a multifunctional autonomous unmanned ground vehicle (MAUGV), aiming to enhance its maneuverability, stability, and obstacle‐overcoming capabilities through the development and integration of control systems, sensor fusion, and modular design. The core problem addressed is the integration of autonomous capabilities with operational effectiveness across diverse and challenging environments. By implementing mathematical modeling and prototype testing, motor characteristics are analyzed, and various operational scenarios are simulated to predict the vehicle’s behavior in real‐world conditions. Results demonstrated the MAUGV’s ability to tackle obstacles up to specific heights and inclines, highlighting the model’s accuracy in replicating physical dynamics and the vehicle’s potential in challenging environments. These outcomes stem from an examination of drive power, traction characteristics, and cross‐country ability, underscoring the model’s utility in enhancing vehicle design and functionality. Additionally, the integration of a bayonet connection for rapid module changes significantly enhances the MAUGV’s adaptability across different missions. The practical implications of this work extend to the development and improvement of autonomous vehicles, offering insights into design considerations that improve adaptability and performance in varied operational contexts. By addressing specific challenges associated with autonomous mobility, the findings contribute to advancing the field, suggesting pathways for future research and application in areas requiring robust, versatile unmanned ground vehicles (UGVs).

Reinforcement Learning (RL), in particular, Proximal Policy Optimization (PPO), and Twin Delayed Deep Deterministic Policy Gradient (TD3), to stabilize a weapon turret system with a set of external disturbances was considered in this study. Compared to traditional control algorithms such as proportional-integral-derivative (PID) controllers, they have their limits in dynamic settings because of manual tuning and a lack of a durable response to disturbances. This research will compare RL-based controllers and PID in a simulated scenario in which the turret will experience disturbances related to recoil, vibrations, and angular oscillations. A kinematic model of the turret was designed, and Lagrangian mechanics were used to model the disturbances of the turret. The run was done in PyBullet alongside the evaluation of the performance via mean absolute error (MAE) and root mean square error (RMSE). These findings show that RL controllers, particularly PPO and TD3, performed better than the PID one in terms of faster stabilization, reduced errors, and compensation of disturbances. RL agents simulated independently to different patterns of disturbances in a noisy and dynamic environment, and performed better than conventional systems. The results prove that RL-based control systems can be used in real-world applications, especially where accuracy is necessary.