Hybrid control strategies for multi-robot systems: enhancing coordination, communication, and adaptability - Nazarenko, V. A.; Romasevych, Y. O. (Energy and Automation, 2025)
Multi-robot systems have garnered substantial interest in industrial automation, search and rescue operations, and space exploration due to their capacity to execute complex tasks with enhanced efficiency and robustness. Effective coordination among multiple autonomous agents is crucial for optimizing task execution, minimizing resource utilization, and ensuring operational reliability. However, key challenges such as dynamic task allocation, collision avoidance, communication constraints, and adaptability to environmental changes persist. This study presents a novel hybrid approach to multi-robot task coordination, integrating swarm intelligence principles with reinforcement learning techniques to enhance decision-making and adaptability in dynamic environments. The proposed methodology employs a hybrid algorithm that synergizes swarm intelligence-based heuristics with reinforcement learning frameworks to achieve optimal task allocation and path planning. The system is implemented in a simulated multi-robot environment, where robots operate under predefined task objectives and varying environmental conditions. The evaluation framework encompasses a series of performance metrics, including task completion time, energy efficiency, inter-robot communication overhead, and system robustness against dynamic perturbations. Comparative analysis is conducted against conventional heuristic and deterministic approaches to validate the effectiveness of the proposed coordination model. The experimental modeling evaluation reveals that the proposed coordination framework significantly enhances task execution efficiency by minimizing redundant movements and optimizing resource allocation. Performance improvements can be measured in reduced task completion time (by an average of X%), lower energy consumption (by Y%), and improved adaptability to unforeseen obstacles. Additionally, the hybrid approach demonstrates superior resilience in dynamic environments, maintaining stable coordination performance despite task demands and variations in environmental unpredictability. Statistical analysis confirms the robustness of the proposed method over traditional strategies, highlighting its applicability in real-world multi-robot deployments. The findings underscore the potential of integrating swarm intelligence and reinforcement learning to achieve scalable and adaptive multi-robot coordination. This approach offers substantial implications for real-world applications, including warehouse logistics, autonomous surveillance, and disaster response operations. Future research will extend real-time adaptability, enhance multi-agent learning capabilities, and scale the framework for larger robotic fleets with decentralized decision-making architectures. Furthermore, potential hardware implementations and real-world testing had been explored to validate simulation findings and refine deployment strategies. Key words: Industrial automation, IoT, Robotics, System Architecture, Control systems, Simulation, Swarm, SLAM