Distributed control for geometric pattern formation of large-scale multirobot systems

Abstract

Introduction: Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarms of drones or robots, and smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly.Methods and result: In this paper, we provide a distributed displacement-based control law that allows large groups of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility.Results: We show the validity and robustness of our approach via numerical simulations and experiments, comparing it, where possible, with other approaches from the existing literature.

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

“…Collaboration necessitates meeting specific criteria, such as sharing each agent's state information and utilizing onboard resources to complete tasks. The context of a hostile site adds complexity, demanding strategies with mathematical intricacy [14], [15], influenced by ecosystem characteristics (multi-robot setups, tasks, region of interest, and resources). In the case of a hostile site with intricate morphology, the challenge intensifies, relying on capabilities like finding a direct path.…”