The Cost of Commuting: Why Greedy Heuristics Outperform Standard Information Gain in Cluttered Environments

Frontier-based exploration is considered as the basis of autonomous multi-robot mapping. While information-theoretic approaches—which prioritize frontiers that could maximize map coverage—are theoretically superior to simple greedy distance-minimizing heuristics, they are often the main reason for significant travel costs, specifically in cluttered environments. In this paper, we investigate the trade-off between information gain and travel distance using a lightweight Python-based simulatior we generated. We compare three distinct exploration strategies: Random, Closest-Frontier (Greedy), and Information Gain. Our initial experiments revealed a counter-intuitive result: standard Information Gain strategies performed worse compared to the Greedy baseline due to excessive traversal times across dense map structures. Through a comprehensive parameter sensitivity analysis, we demonstrate that heavily penalizing distance within the utility function allows Information Gain to converge toward and eventually surpass the Greedy baseline. We report that an optimized Distance-Weighted Information Gain strategy achieves the highest efficiency, demonstrating that in cluttered environments, minimizing “commuter cost” is a prerequisite for leveraging information-theoretic gains.