Control and Scheduling Frameworks for Multi-Human–Multi-Robot Collaboration

Recent technological advances and the increasing demand for production flexibility have driven the widespread adoption of complex automated systems, including redundant and mobile manipulators, in both industrial and everyday life scenarios. These systems are increasingly deployed in unstructured and dynamic environments, where they must operate alongside humans and address complex manipulation and coordination challenges. In this context, ensuring safe, efficient, and minimally invasive human–robot coexistence remains a critical issue, particularly given the limitations of traditional safety mechanisms. This thesis investigates key aspects of human–robot collaboration and interaction in real-world environments, ranging from advanced control methodologies for redundant robotic platforms to task allocation and scheduling strategies for heterogeneous multi-agent systems composed of humans and robots. The proposed contributions include control frameworks that integrate hierarchical optimization and safety constraints, human-safety-oriented control strategies, adaptive shared autonomy approaches, and human-in-the-loop scheduling methods that account for human workload, preferences, and behavioral variability. Furthermore, a hybrid framework combining Large Language Models and constraint-based optimization is introduced to bridge high-level natural language instructions with feasible and optimized multi-agent execution plans. Overall, this work aims to balance task effectiveness and human safety while leveraging the complementary capabilities of human and robotic agents to enhance collaboration, efficiency, and system robustness.