Integrated Model Predictive and Human-Inspired Control for Search-and-Rescue Robotics
Perception, Planning, and Mapping
M. Baglioni (TU Delft - Control & Simulation)
J. Hellendoorn – Promotor (TU Delft - Robust Robot Systems, TU Delft - Cognitive Robotics)
A. Jamshidnejad – Copromotor (TU Delft - Sequential Decision Making)
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Abstract
This PhD thesis addresses the optimal control and AI-based control of Search-and-Rescue (SaR) robots. The work is motivated by the need to improve the efficiency of SaR operations after disasters, using robots. In fact, the main benefits of using SaR robots are reduced cost, improved speed of response, increased search performance, extended reachability to otherwise inaccessible places, and fewer risks for the SaR crew. Robots can optimize the mission plans and safely explore the environment through systematic mathematical approaches. Therefore, novel control approaches are needed to enable
robots to perform SaR operations autonomously and time-efficiently, and this is the main objective of this thesis.
The main contributions of this PhD thesis are the following:
1. We propose novel mission planning frameworks and architectures for ground or flying SaR robots based on Model Predictive Control (MPC), Fuzzy Logic Control (FLC), and other control approaches, in some cases combined to exploit the advantages of multiple methods.
2. We integrate our architectures with models for moving targets and dynamic obstacles, and we leverage robust control formulations to deal with uncertainties and perception approaches to map the SaR environment and track targets.
3. We validate our approaches by comparing them to other state-of-the-art approaches in case studies with simulations and in some cases with real-life experiments in the lab.