The rapid advancement of artificial intelligence (AI), autonomous robotics, and distributed computing has significantly improved the capabilities of multi-robot systems (MRS) across applications such as warehouse automation, disaster response, healthcare, precision agriculture, intelligent transportation, and smart manufacturing. A key challenge in these systems is dynamic task allocation, where robots must efficiently assign and reassign tasks in response to changing environments, communication constraints, resource limitations, and robot failures. Conventional approaches often face limitations in scalability, computational efficiency, and adaptability.This paper proposes an AI-based dynamic task allocation framework that integrates machine learning, reinforcement learning, swarm intelligence, and optimization techniques to enable intelligent and adaptive decision-making in heterogeneous multi-robot systems. The framework considers robot capabilities, task priorities, battery levels, communication quality, travel distance, and workload balancing to optimize real-time task allocation. Reinforcement learning supports adaptive policy learning, while swarm intelligence enables decentralized cooperation. Graph-based task modeling and utility-based optimization further improve resource utilization and minimize execution time, energy consumption, and task conflicts.Experimental evaluation using metrics such as task completion rate, response time, energy efficiency, workload distribution, and scalability demonstrates that the proposed framework outperforms conventional scheduling methods by providing improved adaptability, fault tolerance, and operational efficiency. The proposed approach offers a scalable and intelligent solution for next-generation multi-robot collaboration in Industry 5.0 and cyber-physical systems.
Alexey Lyapunov· International Journal of Int...· 0 citations
We focus on autonomous inspection robots operating in complex, dynamic, and GNSS-denied industrial environments dealing with critical problems related to real-time trajectory optimization, feature tracking and precise spatial localization. Remember that traditional control algorithms tend to not adapt well to difficult visual occlusions, non-Gaussian sensor noise, or unforeseen structural impediments. We propose a unified AI pipeline that fuses deep reinforcement learning with sensor data—by combining Light Detection and Ranging (LiDAR), Visual-Inertial Odometry (VIO), and thermal images—to discover flexible navigation strategies for autonomous inspection ground vehicles. In this work, we form a prior method based on Deep Deterministic Policy Gradient controller and a adaptive unscented Kalman filter which continuously providing constantly estimating robot states and improving the motion primitives in hazardous operating conditions. A series of experimental evaluations conducted on both simulated industrial plants and a physical mock-up facility show that the AI-based method achieves up to 51% relative reduction in localization error compared to traditional Simultaneous Localization and Mapping methods. The results show that the path deviation decrease by 38.15%, collision avoidance timeliness is significantly improved, and the accuracy of anomalies detection can reach more than 98%. These results validate that end-to-end AI navigation architectures deliver the robust performance needed for next-gen automated industrial monitoring and non-destructive evaluation at scale.
Andrey Ershov, Alexey Lyapunov· International Journal of Int...· 0 citations
The rapid adoption of electric vehicles (EVs) has increased the need for intelligent charging infrastructure capable of addressing challenges such as charging congestion, uneven energy distribution, grid instability, and long waiting times. Conventional charging management approaches based on static scheduling are inadequate for dynamic charging environments. This paper proposes a Digital Twin-Assisted Optimization Framework for Electric Vehicle Charging Infrastructure (DTO-EVCI) that integrates IoT, cloud computing, artificial intelligence (AI), machine learning, and optimization techniques to enable real-time monitoring, predictive analytics, and intelligent charging management. The framework synchronizes physical charging stations with a virtual digital twin, enabling accurate simulation, charging demand prediction, occupancy forecasting, optimized scheduling, predictive maintenance, and adaptive energy management. By improving charging efficiency, resource utilization, grid reliability, and renewable energy integration, the proposed framework reduces operational costs, minimizes charging delays, and supports sustainable large-scale EV deployment while contributing to smart city and carbon-neutral transportation initiatives.
Andrey Ershov, Alexey Lyapunov· International Journal of Mod...· 0 citations