EARS-Info：面向多机器人探索的信息感知事件驱动重分配方法
EARS-Info: Information-Aware Event-Driven Reallocation for Multi-Robot Exploration
AI 导读
研究者提出 EARS-Info，这是事件驱动蚂蚁重分配搜索（EARS）在多机器人地图探索中的信息感知扩展，结合边际感知增益、可行重定位、回程感知路由、感知预留与缓存最短路径。
正文 · 原文
Abstract:We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids. It combines marginal sensing gain, feasible relocation, return-aware routing, sensing reservations, and cached shortest paths. Across 168,000 episodes comparing 30 implementations on eight city maps, EARS-Info achieves the highest mean operational score in every evaluation panel. With eight robots and 3% communication dropout, it improves the score over EARS-Safe by 5.43%, increases weighted discovery from 88.88% to 98.46%, and reduces mean energy use by 2.62%. Score differences are positive in all eight cities ($p=0.0078125$; descriptive 95% city-bootstrap interval $[0.0159,0.0694]$), with no simulated incidents across 5,600 EARS-Info episodes. These results support the complete controller; individual component advantages remain uncertain. Generalization is limited by reused geography, one training replicate, coarse maps, and adapted comparison methods.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.38040 [cs.RO] |
| (or arXiv:2610.38040v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38040 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vahid Tavakkoli [view email]
[v1]
Tue, 27 Oct 2026 22:51:53 UTC (97 KB)
来源：arXiv cs.AI · arxiv.org