Mobile robots in unknown cluttered environments with irregularly shaped obstacles often face sensing, energy, and communication challenges which directly affect their ability to explore these environments. In this paper, we introduce a novel deep learning method, Confidence-Aware Contrastive Conditional Consistency Model (4CNet), for mobile robot map prediction during resource-limited exploration in multi-robot environments. 4CNet uniquely incorporates: 1) a conditional consistency model for map prediction in irregularly shaped unknown regions, 2) a contrastive map-trajectory pretraining framework for a trajectory encoder that extracts spatial information from the trajectories of nearby robots during map prediction, and 3) a confidence network to measure the uncertainty of map prediction for effective exploration under resource constraints. We incorporate 4CNet within our proposed robot exploration with map prediction architecture, 4CNet-E. We then conduct extensive comparison studies with 4CNet-E and state-of-the-art heuristic and learning methods to investigate both map prediction and exploration performance in environments consisting of uneven terrain and irregularly shaped obstacles. Results showed that 4CNet-E obtained statistically significant higher prediction accuracy and area coverage with varying environment sizes, number of robots, energy budgets, and communication limitations. Real-world mobile robot experiments were performed and validated the feasibility and generalizability of 4CNet-E for mobile robot map prediction and exploration.

翻译：在未知杂波环境（包含不规则形状障碍物）中，移动机器人常面临感知、能量和通信挑战，这些因素直接影响其探索环境的能力。本文提出一种新颖的深度学习方法——置信感知对比条件一致性模型（4CNet），用于多机器人环境下资源受限探索过程中的移动机器人地图预测。4CNet独特地融合了：1）用于预测不规则未知区域地图的条件一致性模型；2）对比式地图-轨迹预训练框架，该框架为轨迹编码器提取地图预测过程中邻近机器人轨迹的空间信息；3）用于测量地图预测不确定性的置信网络，以实现资源约束下的高效探索。我们将4CNet嵌入所提出的带地图预测的机器人探索架构4CNet-E中。随后，通过4CNet-E与当前最先进的启发式及学习方法开展广泛对比研究，考察其在包含不平坦地形和不规则形状障碍物的环境中的地图预测与探索性能。结果表明：在不同环境规模、机器人数量、能量预算和通信限制条件下，4CNet-E在预测精度和覆盖率上均取得统计显著性提升。真实环境移动机器人实验验证了4CNet-E在移动机器人地图预测与探索中的可行性与泛化能力。