ARGOS Lab
Thesis Opportunities
Thesis topics currently open to Master students at ARGOS Lab. To apply, send a CV and a short paragraph presenting yourself and your motivation to the supervisors listed for the topic: the contact button on each topic opens a pre-addressed e-mail.
Master Coordinated Motion of Arresting Cables for Passive Rocket Capture through Sequential Convex Programming
Research question
How can we extend Sequential Convex Programming to the coordinated planning of multiple actors in complex recovery scenarios?
Coordinated Motion of Arresting Cables for Passive Rocket Capture through Sequential Convex Programming
Research question
How can we extend Sequential Convex Programming to the coordinated planning of multiple actors in complex recovery scenarios?
The idea is to extend to the coordinated planning of both the sliders of the robotic arms and the 6-dof rocket motion the use of Sequential Convex Programming, to come up with a unique framework able to coordinate the multi-actor system for super heavy systems.
Supervisor
Marco Sagliano
Master Coordinated Robotic-arms capture of a Super-Heavy-like Reusable Rocket through Sequential Convex Programming
Research question
How can we extend Sequential Convex Programming to the coordinated planning of multiple actors in complex recovery scenarios?
Coordinated Robotic-arms capture of a Super-Heavy-like Reusable Rocket through Sequential Convex Programming
Research question
How can we extend Sequential Convex Programming to the coordinated planning of multiple actors in complex recovery scenarios?
The idea is to extend to the coordinated planning of both the robotic arm and the 6-dof rocket motion the use of Sequential Convex Programming, to come up with a unique framework able to coordinate the multi-actor system for super heavy-boosters-like systems.
Supervisor
Marco Sagliano
Master Smarter Grids for Faster Trajectories: Adaptive Mesh Refinement for Pseudospectral Optimal Control
Research question
How can we automatically adapt the discretization of pseudospectral optimal control problems to achieve high accuracy with the smallest possible computational effort?
Smarter Grids for Faster Trajectories: Adaptive Mesh Refinement for Pseudospectral Optimal Control
Research question
How can we automatically adapt the discretization of pseudospectral optimal control problems to achieve high accuracy with the smallest possible computational effort?
Pseudospectral methods can accurately solve complex trajectory optimization problems, but their efficiency strongly depends on how the discretization nodes are distributed. The idea of this thesis is to investigate adaptive mesh-refinement strategies to identify where a trajectory requires additional resolution and where the mesh can remain coarse. The goal is to develop, implement, and test new refinement algorithms to reduce the computational cost of pseudospectral optimal control while retaining accuracy, with applications to challenging aerospace trajectory optimization problems.
Supervisors
Riccardo Minnozzi Marco Sagliano
Have a subject of your own?
The topics above are the ones we are actively looking to assign, but they are not the only possibilities. If you have a subject of your own that falls within the research areas of the lab, we are glad to discuss it. Get in touch with a short description of what you would like to work on, together with your CV.
Propose a subject