# Dispersion of Mobile Robots: The Power of Randomness

**Authors:** Anisur Rahaman Molla, William K. Moses Jr

arXiv: 1902.10489 · 2019-02-28

## TL;DR

This paper demonstrates that using randomness allows mobile robots to disperse efficiently on graphs with minimal memory, matching lower bounds and improving over deterministic approaches.

## Contribution

It shows that randomized algorithms can achieve dispersion with O(log Δ) memory, matching lower bounds, unlike deterministic methods requiring Ω(log n) memory.

## Key findings

- Randomized algorithms achieve dispersion with O(log Δ) bits of memory.
- Lower bound of Ω(log Δ) bits for randomized dispersion algorithms.
- Extension to k-dispersion problem with multiple robots per node.

## Abstract

We consider cooperation among insects, modeled as cooperation between mobile robots on a graph. Within this setting, we consider the problem of mobile robot dispersion on graphs. The study of mobile robots on a graph is an interesting paradigm with many interesting problems and applications. The problem of dispersion in this context, introduced by Augustine and Moses Jr., asks that $n$ robots, initially placed arbitrarily on an $n$ node graph, work together to quickly reach a configuration with exactly one robot at each node. Previous work on this problem has looked at the trade-off between the time to achieve dispersion and the amount of memory required by each robot. However, the trade-off was analyzed for \textit{deterministic algorithms} and the minimum memory required to achieve dispersion was found to be $\Omega(\log n)$ bits at each robot. In this paper, we show that by harnessing the power of \textit{randomness}, one can achieve dispersion with $O(\log \Delta)$ bits of memory at each robot, where $\Delta$ is the maximum degree of the graph. Furthermore, we show a matching lower bound of $\Omega(\log \Delta)$ bits for any \textit{randomized algorithm} to solve dispersion.   We further extend the problem to a general $k$-dispersion problem where $k> n$ robots need to disperse over $n$ nodes such that at most $\lceil k/n \rceil$ robots are at each node in the final configuration.

## Full text

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## References

29 references — full list in the complete paper: https://tomesphere.com/paper/1902.10489/full.md

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Source: https://tomesphere.com/paper/1902.10489