This dissertation explores the development of sophisticated distributed layered control algorithms focused on the navigation, planning, and control of multi-agent quadrupedal robots collaborating in uncertain environments. Quadrupedal robots are high-dimensional, complex systems that are inherently unstable, posing significant challenges in designing predictive control laws. Template models offer a solution by providing a bridging layer of reduced-order models with fewer state variables and linearized dynamics. However, this approach compromises the agility and full potential of these sophisticated machines, as template models may fail to capture the intricate nonlinear dynamics of quadrupedal robots. Furthermore, in multi-robot systems (MRS) where numerous robots operate concurrently, it becomes crucial to develop strategies embedding collision safety mechanisms. One approach involves embedding Euclidean distance constraints in the predictive control formulation. While effective, this method significantly complicates the optimal control (OC) problem and increases computational overhead.
To mitigate these challenges, this dissertation explores hierarchical and distributed control frameworks, focusing on developing real-time feasible controllers that guarantee collision avoidance while preserving the agility of these hardware platforms by utilizing fully nonlinear template models. In particular, this research investigates a multi-layered framework consisting of potential fields at the high-level layer, a distributed nonlinear model predictive control (DNMPC) based middle-level layer responsible for uncertainty mitigation, and full-order nonlinear controllers at the low-level layer. Additionally, the latter part of this dissertation examines the integration of safety-ensuring control barrier functions (CBFs) into the nonlinear model predictive control (NMPC) layer, thereby providing rigorous mathematical guarantees for collision avoidance.
The crux of this research lies in addressing the following questions: How do we design layered control frameworks to guarantee optimal gait planning and collision avoidance while maintaining computational tractability? How do we mitigate uncertainty in the environment in real-time using safety-critical control algorithms? / Doctor of Philosophy / This dissertation investigates advanced control strategies for coordinating teams of quadrupedal robots in dynamic and uncertain environments. Quadrupedal robots present significant challenges in control and stability due to their complex, high-dimensional nature and inherent instability. Current approaches often employ simplified models for control, which, while computationally efficient, fail to fully capture the intricate dynamics of these sophisticated machines, thus limiting their agility and potential. Furthermore, in multi-robot systems, ensuring collision avoidance is a practically integral part of control. Conventional methods, including Euclidean distance constraints for collision avoidance, prove effective but substantially increase computational demands and complicate the optimal control problem. To address these challenges, this research explores a hierarchical, distributed control framework designed to guarantee collision-free navigation while maximizing the agility of quadrupedal platforms through the use of comprehensive nonlinear models. The proposed framework consists of three primary layers: a high-level layer utilizing potential fields for global path planning, a middle layer employing distributed nonlinear model predictive control for local navigation and uncertainty mitigation, and a low-level layer implementing full-order nonlinear controllers for precise motion execution. Additionally, this work examines the integration of control barrier functions into the predictive control layer, providing mathematical guarantees for collision avoidance. The core objectives of this research are twofold: first, to develop layered control frameworks that ensure optimal gait planning and collision avoidance while maintaining computational feasibility; and second, to create real-time algorithms capable of mitigating environmental uncertainties using safety-critical control methods. By addressing these fundamental questions, this dissertation aims to advance the field of multi-agent quadrupedal robotics, enhancing the capability of robotic teams to operate effectively in complex, unpredictable environments. The potential applications of this research extend to critical areas such as search and rescue operations, environmental monitoring, and exploration of hazardous terrains.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/121227 |
| Date | 30 September 2024 |
| Creators | Imran, Basit Muhammad |
| Contributors | Mechanical Engineering, Akbari Hamed, Kaveh, Leonessa, Alexander, Losey, Dylan Patrick, L'Afflitto, Andrea |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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