Reference : Optimal Motion Planning and Control with Safety Guarantees for Aerial Robots
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Aerospace & aeronautics engineering
Computational Sciences
Optimal Motion Planning and Control with Safety Guarantees for Aerial Robots
Castillo López, Manuel mailto [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) > > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation and Robotics Research Group (ARG)]
University of Luxembourg, ​Luxembourg, ​​Luxembourg
Docteur en Sciences de l'Ingénieur
Voos, Holger
Hadji-Minaglou, Jean-Régis
Olivares Mendez, Miguel Angel
Darouach, Mohamed
Kamgarpour, Maryam
[en] Stochastic Optimal Control ; Model Predictive Control ; Aerial Robot
[en] Autonomous aerial robots are expected to revolutionize many industries, such as construction, transportation or even space exploration. However, to target an industry where different robots and humans are meant to share the same space our algorithms need to provide safety and efficiency guarantees. Navigating autonomously in these kind of environments poses a great challenge. We may face a significant number of obstacles, and we can only estimate where they are and where they are expected to be, but not exactly. Dealing with these uncertainties is a challenging problem in most robotics applications, including motion planning and control. During the last decade, major contributions have established the theoretical basis upon which optimal motion planning and control with safety guarantees can be achieved. However, they involve a high computational cost that scales exponentially with the number of obstacles, rendering a limited domain of robotic applications. The main contribution of this thesis provides an efficient, scalable and safe approximation to this problem, allowing its application to embedded systems with fast dynamics such as aerial robots. This thesis also includes an additional contribution that allow these methods to plan longer trajectories with a minimal computational footprint, allowing to better anticipate evasive maneuvers. These contributions have been validated mathematically, in simulation and in real-time operation on aerial robots, handling uncertain dynamic obstacles such as pedestrians.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Researchers ; Professionals ; Students

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