外观
论文arXiv 2610.2290512 天前
SenseSwarm：通过带共享世界预期的神经结构估计实现快速建图的群体探索
原文链接：SenseSwarm: Swarm Exploration via Neural Structure Estimation with Shared World-Anticipation for Rapid Mapping（arXiv 2610.22905 · 发布日期：2026-10-16）
SenseSwarm: Swarm Exploration via Neural Structure Estimation with Shared World-Anticipation for Rapid Mapping
SenseSwarm在共享占据地图上预测未知结构并引导去中心化群体，在真实楼层基准的仿真中加速地图重建。
核心要点
- 群体机器人虽能更快覆盖建筑，但地图完整度受限于已实际到达的空间。
- 采用709K参数预测器，基于群体多前沿观测微调，并并入共享引导场。
- 在1620次仿真中，非预测基线达到80%已知地图需长1.3至1.9倍。
- 共享地图上仅计算一次预测，32至512机器人时代价保持不变且接触率更低。
验证范围与局限：验证仅限十二个真实建筑楼层基准上的仿真，群体规模为32至512。
英文摘要 (Abstract)
A robot swarm covers a building faster with more robots, but the map is only as complete as the space the robots have physically visited. We target the amount of an unknown building a decentralized swarm knows at each instant, ahead of physical coverage. SenseSwarm serves a lightweight learned map predictor on the swarm's shared occupancy map and folds the predicted structure back into a stigmergic guidance field. Its 709K-parameter predictor is fine-tuned on the swarm's own multi-front observations, which we show is necessary: a single-robot predictor collapses on such views. We build a benchmark of twelve real building floors from HouseExpo, HKUST, and KTH, up to 24,000 square meters, and run 1,620 simulations across nine coordination methods, swarm sizes 32 to 512, and three seeds. On physical coverage the swarm is near parity with a greedy frontier planner, as expected. On map reconstruction, every non-predictive baseline needs 1.3 to 1.9 times longer to reach an 80 percent known map, and SenseSwarm holds an equal-time area lead of about 670 square meters, up to 1,359. Folding the prediction into the shared field matters: the same predictor applied per robot matches our speed but suffers a 6.9 times higher inter-robot contact rate at 512 robots. The prediction is computed once per shared map, so its cost stayed flat from 32 to 512 robots.
作者：Haohua Que, Haojia Gao, Mingkai Liu, Hoiian Au, Qian Zhang, Jiayue Xie, Yule Cai, 陈博奎, Xueqian Wang, Fei Qiao
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