Distributed Computing by Mobile Robots: Expanding the Horizon (Invited Talk)

Abstract

Extensive research focus within distributed computing has been spent on the computational and complexity issues arising in systems of mobile computational entities (called robots) operating in the Euclidean space in Look-Compute-Move cycles. In the classical OBLOT model, the robots are homogeneous, having no distinguishing features and running the same algorithm. Moreover, they are silent, having no explicit means of communication, and oblivious, meaning that, whenever activated, they forget everything they have seen and done in previous cycles.
The research focus has been in determining the impact that internal capabilities (e.g., memory, communication) and external conditions (e.g. synchrony, type of the activation scheduler) have on the computability power of these robots (e.g., see [P. Flocchini et al., ed., 2019] and chapters therein). Over the years, various enhancement of the basic model have been studied in regards to memory and communication under the different activation schedules (e.g., [K. Buchin et al., 2021; K. Buchin et al., 2022; S. Das et al., 2016; P. Flocchini et al., 2023; P. Flocchini et al., 2016]). At the same time, the computational landscape has been broadened by examining aspects typically explored in other areas of distributed computing that have not yet been investigated in these systems. One such aspect is the concept of robots possessing identifiers (which need not be identical), diverging from the usual assumption of homogeneity (e.g., [Y. Asahiro and M. Yamashita, 2023; S. Bhagat et al., 2020; P. Flocchini et al., 2024a; P. Flocchini et al., 2024b; H. Seike and Y. Yamauchi, 2023]).
In this talk, I will first discuss some of the recent results shaping the overall computational landscape. I will then describe some recent explorations on the impact of introducing non-homogeneity of the robots.