M. Baglioni
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Integrated Model Predictive and Human-Inspired Control for Search-and-Rescue Robotics
Perception, Planning, and Mapping
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. ...
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.
Robots will bring Search and Rescue (SaR) in disaster response to another level, in case they can autonomously take over dangerous SaR tasks from humans. A main challenge for autonomous SaR robots is to safely navigate in cluttered environments with uncertainties, while avoiding static and moving obstacles. We propose an integrated control framework for SaR robots in dynamic, uncertain environments, including a computationally efficient heuristic motion planning system that provides a nominal (assuming there are no uncertainties) collision-free trajectory for SaR robots and a robust motion tracking system that steers the robot to track this reference trajectory, taking into account the impact of uncertainties. The control architecture guarantees a balanced trade-off among various SaR objectives, while handling the hard constraints, including safety. The results of various computer-based simulations, presented in this paper, showed significant out-performance (of up to 42.3%) of the proposed integrated control architecture compared to two commonly used state-of-the-art methods (Rapidly-exploring Random Tree and Artificial Potential Function) in reaching targets (e.g., trapped victims in SaR) safely, collision-free, and in the shortest possible time.
Search and rescue (SaR) is challenging, due to the unknown environmental situation after disasters occur. Robotics has become indispensable for precise mapping of the environment and for locating the victims. Combining flying and ground robots more effectively serves this purpose, due to their complementary features in terms of viewpoint and maneuvering. To this end, a novel, cost-effective framework for mapping unknown environments is introduced that leverages You Only Look Once and video streams transmitted by a ground and a flying robot. The integrated mapping approach is for performing three crucial SaR tasks: localizing the victims, i.e., determining their position in the environment and their body pose, tracking the moving victims, and providing a map of the ground elevation that assists both the ground robot and the SaR crew in navigating the SaR environment. In real-life experiments at the CyberZoo of the Delft University of Technology, the framework proved very effective and precise for all these tasks, particularly in occluded and complex environments.