Currently, active work is underway to introduce a project-based approach to the management of new product production processes. However, even if the need to implement project management is undeniable, the question arises of the pitfalls in implementing these innovations into the defense enterprises. Based on this, this work aims to determine organizational problems of implementing project management system into an enterprise of the defense industry. The following methods were used to collect data: in-depth interviews with employees of the company, taking a direct and key part in the PM processes; study of internal regulatory and other documentation on the research object; conducting a survey of employees. The research revealed the use of project management tools that are developed mostly in procurement management. The main problems of project management implementation are in scheduling, communications and integration management. The weaknesses in terms of the support provided for the implementation of projects by the enterprise include such areas as the availability of standard project management software and the organization of permanent trainings on project management. Based on the problems revealed, some recommendations are made for implementation of project management system in the enterprise of the defense industry.

Nowadays, leading world companies widely use methodology, standards, and project management tools in many areas of project-oriented activities, including investment, innovation, and information projects. Last years the implementation and functioning of the country’s enterprises’ project management system have been actively discussed in Kazakhstan.This work aims to identify critical processes of project management planning that affect the effectiveness of projects at the enterprise of the military-industrial complex of Kazakhstan. For this purpose, a survey of top managers of defense industry companies was conducted. A total of 28 respondents were interviewed, working at 18 enterprises of the military-industrial complex of the Republic of Kazakhstan and directly participating in the implementation of projects. Data were personally collected by a questionnaire survey conducted during 2019. To verify the formulated model’s assumptions and success, correlation analysis, and other relevant tests were used. Using the model of project management planning quality (PMPQ), process efficiency has been assessed, and critical factors for the effectiveness of defense industry projects have also been identified. The results show that the Republic of Kazakhstan’s defense industry complex projects has a low level of efficiency and an average score for the use of project planning processes. Project efficiency evaluation has also revealed critical knowledge areas for defense projects like human resource management, schedule development, and define scope. The research expands knowledge in PM, revealing the importance of planning processes for the defense industry that need more focus to achieve top-level success and effectiveness of projects. AcknowledgmentThis study is the second part of grant AP05134488 that has been funded by the Ministry of Education and Science of the Republic of Kazakhstan.

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.

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.