Maximal Exploration of Trees with Energy-Constrained Agents

TL;DR

This paper presents an algorithm for multiple energy-limited agents to explore unknown trees efficiently, achieving near-optimal coverage with proven competitive bounds.

Contribution

It introduces a new exploration algorithm for energy-constrained agents that balances exploration strategies and provides competitive ratio bounds.

Findings

The proposed algorithm is 3-competitive compared to an optimal offline solution.

No online algorithm can have a competitive ratio better than 2.17.

The algorithm effectively balances breadth-first and depth-first exploration strategies.

Abstract

We consider the problem of exploring an unknown tree with a team of $k$ initially colocated mobile agents. Each agent has limited energy and cannot, as a result, traverse more than $B$ edges. The goal is to maximize the number of nodes collectively visited by all agents during the execution. Initially, the agents have no knowledge about the structure of the tree, but they gradually discover the topology as they traverse new edges. We assume that the agents can communicate with each other at arbitrary distances. Therefore the knowledge obtained by one agent after traversing an edge is instantaneously transmitted to the other agents. We propose an algorithm that divides the tree into subtrees during the exploration process and makes a careful trade-off between breadth-first and depth-first exploration. We show that our algorithm is 3-competitive compared to an optimal solution that we could obtain if we knew the map of the tree in advance. While it is easy to see that no algorithm can be better than 2-competitive, we give a non-trivial lower bound of 2.17 on the competitive ratio of any online algorithm.