About the project
Objective
Modern autonomous systems are required to operate in close collaboration with human operators in complex, dynamic environments, such as industrial settings, maritime operations, and disaster-response scenarios. In these contexts, autonomy must remain safe, adaptive, and responsive to human inputs, environmental disturbances, and evolving task specifications in real time.
The project aims to develop a unified control framework that enables autonomous systems to safely and adaptively collaborate with humans in complex and unstructured environments. Specifically, it will bridge high-level task allocation and trajectory planning with low-level feedback control using the mathematical paradigm of hybrid systems, and it will enable interactive decision-making and human supervisory inputs through the integration of game theoretic principles.
The project will further translate this theoretical framework into computationally tractable controllers suitable for real-time implementation on mobile autonomous systems. The proposed approach will be validated through real-world experiments in maritime robotics scenarios, including diver–robot collaboration missions in both controlled test tanks and open-water environments. By integrating new theory and experimental deployment, the project seeks to enable trustworthy, resilient, and adaptive human-in-the-loop autonomy across multiple time scales, from high-level decision-making to low-level feedback control.
Background
In recent years, autonomous systems have been increasingly deployed to work alongside human operators in executing complex, coordinated tasks. Prominent examples include warehouse automation, assembly-line operations, and intelligent logistics, where systems must handle tasks such as sorting, inspection, or replenishment under strict spatial constraints (e.g., limited workspaces and predefined pathways) and temporal requirements (e.g., synchronization and time-critical deliveries).
These challenges become even more pronounced in extreme environments, including underwater robotics, space exploration, and disaster-response scenarios, where systems operate in unstructured and uncertain conditions characterized by limited communication, poor visibility, and strong environmental disturbances. Across both industrial and extreme domains, the ability to adapt task allocation, planning, and control in real time is essential to ensure efficiency, resilience, and safety in human–robot collaboration.
Autonomous systems are increasingly employed alongside humans to perform complex tasks in both structured and unstructured environments.
The state-of-the-art control architecture for autonomous systems typically follows a layered structure consisting of task allocation, trajectory planning, and real-time low-level feedback control. Task allocation determines how tasks are distributed among agents, planning generates trajectories that satisfy spatial and temporal specifications, and low-level control ensures accurate tracking and safety. While effective in structured settings, this hierarchical stack is not inherently designed to account for real-time human intervention, changing objectives, or unexpected obstacles, which are central features of human-in-the-loop scenarios.
Cross-disciplinary collaboration
The project brings together complementary expertise in applied mathematics (hybrid systems, formal methods, and game theory), control engineering (model predictive control), and maritime and underwater robotics to address the challenges of human-in-the-loop autonomous systems in extreme environments. This cross-disciplinary collaboration tightly integrates mathematical modelling, control design, and real-world robotic experimentation.
PIs: Giuseppe Belgioioso, Dimos Dimarogonas, and Ivan Stenius.
