Distributed Algorithms for Multi-robot Autonomy

Autonomous robots can perform dangerous and tedious tasks, eliminating the need for human involvement. To deploy an autonomous robot in the field, a typical planning and control hierarchy is used, consisting of a high-level planner, a mid-level motion planner, and a low-level tracking controller. In applications such as simultaneous localization and mapping, package delivery, logistics, and surveillance, a group of autonomous robots can be more efficient and resilient than a single robot. However, deploying a multi-robot team by directly aggregating each robot's planning hierarchy into a larger, centralized hierarchy faces challenges related to scalability, resilience, and real-time computation. Distributed algorithms offer a promising solution for introducing effective coordination within a network of robots, addressing these issues. This thesis explores the application of distributed algorithms in multi-robot systems, focusing on several essential components required to enable distributed multi-robot coordination, both in general terms and for specific applications.