Predictive and Vision-based Control for Multi-Agent Aerial and Space Systems
Time: Tue 2024-10-08 14.00
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Video link: https://kth-se.zoom.us/j/65515055298
Language: English
Subject area: Electrical Engineering
Doctoral student: Pedro Roque , Reglerteknik
Opponent: Professor Paolo Robuffo Giordano, IRISA, INRIA Rennes
Supervisor: Professor Dimos V. Dimarogonas, Reglerteknik; Professor Mikael Johansson, Reglerteknik; Associate Professor Jana Tumova, Robotik, perception och lärande, RPL
QC 20240917
Abstract
Autonomous aerial vehicles have been increasingly used and adapted to cover tasks in multiple areas of our daily lives. This growth is mainly due to the rise in computational power in small form factor processing units, making these more efficient and enabling the deployment of novel algorithms for advanced applications. For the same reasons, the deployment of spacecraft for low-orbit operations has experienced an exponential increase, associated with the lower costs of access to orbit and the need for space-bound security and telecommunication. As these systems become ubiquitous, the need to ensure their safe operation in shared environments is ever more significant. In this thesis, we focus on four core problems of multi-agent autonomous systems: robust control, decentralized control for collaborative transportation, formation control, and reliable validation.
To tackle the first problem, we propose a robust control framework for trajectory tracking. Planners onboard the vehicle generate trajectories focusing on a high-level task. Our solution focuses on safe trajectory tracking under disturbances and provides upper bounds on the error with respect to the trajectory. Given a worst-case tracking error, it is possible to plan collision-free trajectories, ensuring system safety.
For the second problem, we propose controllers for collaborative load transportation using aerial vehicles and spacecraft. We develop centralized and decentralized model predictive controllers and compare their performance regarding tracking error and computational tractability. These comparisons allow us to observe the worst-case scenarios for such control architectures and provide an informed view of possible trade-offs in demanding transportation applications.
On the third problem, we focus on control methods for formation control using imaging sensors and informed neighbor motion prediction. We propose a decentralized model predictive controller based on collaborative behavior among the agents in the formation. Then, we develop a formation controller that uses image features and one-range measurement to coordinate the agents.
The last problem of this thesis introduces a novel architecture for ground space robotics testbeds. We propose modular platforms, driven by open-source firmware and software stacks, capable of mimicking spacecraft operating inside space stations or in orbit conditions. We validate these systems in a new space robotics facility at KTH built during this thesis. Lastly, we conclude this monograph with a short analysis of the achieved objectives and an overview of future research directions.