Available at: https://digitalcommons.calpoly.edu/theses/3027
Date of Award
6-2025
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Siavash Farzan
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Networked control systems for multi-agent robotics have emerged as a critical paradigm for executing complex coordinated tasks in diverse environments. While formation control serves as the backbone of such systems, real-world deployment introduces significant challenges including communication constraints, environmental obstacles, and the need for adaptive reconfiguration. This research addresses these challenges by developing a novel unified framework that seamlessly integrates obstacle avoidance algorithms with dynamic formation reconfiguration capabilities, specifically designed for communication-limited networked control architectures. The proposed framework represents a significant advancement over existing approaches by simultaneously handling both static and dynamic obstacles while maintaining system cohesion under communication constraints. Our contribution is threefold: (1) development of a robust networked control architecture that preserves formation integrity despite communication limitations, (2) implementation of a computationally efficient dynamic reconfiguration mechanism with optimal agent reallocation during formation transitions, and (3) formal safety guarantees through control barrier functions for both inter-agent and obstacle collision avoidance. The theoretical foundations of our approach are rigorously established through Lyapunov stability analysis to provide guaranteed convergence properties of the networked multi-agent system under the proposed control laws. Extensive validation through simulations and hardware implementation on physical robotic platforms demonstrates the framework’s superior performance in maintaining safety constraints while achieving formation objectives across diverse obstacle scenarios.