Sensory-based hierarchical control of intelligent multi-vehicle systems​

April 2023 – April 2025

Objective
The research goal of this proposal is to develop a novel hierarchical control framework that enables multi-vehicle systems (MVS) to distribute, manage, and execute complex tasks in possibly dynamic and unstructured environments with safety guarantees and computational efficiency. For specified low-level navigation tasks,  a unified theory will be developed devoted to distributed estimation and formation tracking control problems endowed with reactive collision avoidance abilities based on onboard local sensors (e.g., cameras or range finders). Decision-making mechanisms will be integrated to coordinate high-level navigation tasks to provide MVS with the capability to cooperatively specify the overall task and competitively allocate sub-tasks for each vehicle while ensuring efficient time and energy consumption.

The proposed framework will also enable a set (possibly the whole group) of agents to instantaneously choose and execute between different tasks reacting to real-time environmental changes in a prescribed mission. The innovative framework and algorithms will be developed with rigorous mathematical analysis and realistic simulations, potentially including engineered implementations that address practical urban challenges like search and rescue and intelligent transportation using aerial and sidewalk robots.

Background
Recent decades have witnessed the rapid expansion of autonomous robotic vehicles, such as self-driving cars, drones, autonomous marine vehicles, etc. They are envisioned as essential tools to venture into unsafe areas, enhance human sensory and manipulation abilities, or explore unstructured and possibly dangerous environments. Since a single autonomous agent typically makes it impossible to perform tasks in vast areas or complicated tasks that have to be decomposed into sub-tasks performed by multiple agents, both industry and academia have shown a great interest in the scientific area of networked autonomous vehicle systems, which are systems that can interact and coordinate with each other.

Although academic work on controlled multi-vehicle systems (MVS) has become ever-expanding recently, a large gap exists between current capabilities and required ones in real-world scenarios. For MVS to perform a wide range of tasks, autonomous navigation and control is a fundamental ability under which each vehicle should interact safely with neighbour vehicles and the surrounding environment. However, most existing control algorithms rely on full-state measurements, limiting their applicability to specific applications with suitably equipped experimental areas.

GPS signal is typically unreliable for practical scenarios involving tasks in urban canyons or congested environments. Hence, robust and computationally efficient distributed controllers and estimation algorithms must be designed based on onboard exteroceptive sensors (such as laser range finders, vision, and acoustic sensors). However, the documented results for robotic vehicles using onboard sensors have been limited to ad hoc scenarios. Moreover, the current practice of MVS is often conducting simple missions with restricted autonomy based on offline and centralized supervision and planning, assuming the environment is static and known, such as lighting shows via drones and delivering in warehouses via mobile robots.

About the Digital Futures Postdoc Fellow
Zhiqi Tang is a Digital Futures Postdoctoral Fellow at the Division of Decision and Control Systems of KTH Royal Institute of Technology, Sweden. From 2021 to 2022, she was a postdoctoral researcher with the Institute for Systems and Robotics. She was an Invited Assistant Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico (IST) in Portugal.

She earned a double PhD in Automatic Control and Robotics from IST, University of Lisbon, Portugal, and I3S-CNRS, Université Côte d’Azur, France, in 2021. She obtained her B.S. in Electrical and Computer Engineering from the University of Macau, Macau SAR, China, in 2015.

Her research interests focus on the estimation, control, and decision-making in multi-agent systems, with applications in Robotics and Transportation Systems.

Main supervisor
Jonas Mårtensson, Associate Professor, Division of Decision and Control Systems at KTH

Co-supervisor
Karl H. Johansson, Professor, Division of Decision and Control Systems at KTH
Michele Simoni, Assistant Professor, Transport Systems Analysis at KTH

Watch the recorded presentation at the Digitalize in Stockholm 2023 event: