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 architecture, Confidence-Aware Contrastive Conditional Consistency Model (4CNet), for robot map prediction during decentralized, resource-limited multi-robot exploration. 4CNet uniquely incorporates: 1) a conditional consistency model for map prediction in unstructured 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 irregularly shaped obstacles and uneven terrain. 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. Hardware experiments were performed and validated the applicability and generalizability of 4CNet-E in both unstructured indoor and real natural outdoor environments.

翻译：在未知杂乱环境中，移动机器人常面临不规则形状障碍物带来的感知、能耗与通信挑战，这些因素直接影响其环境探索能力。本文提出一种新颖的深度学习架构——置信感知对比条件一致性模型（4CNet），用于在资源受限的去中心化多机器人探索场景中进行机器人地图预测。该模型创新性地融合了三个核心组件：1）针对非结构化未知区域地图预测的条件一致性模型；2）基于对比式地图-轨迹预训练的轨迹编码器框架，可在预测过程中从邻近机器人轨迹中提取空间信息；3）用于量化地图预测不确定性的置信网络，以支持资源约束下的高效探索。我们将4CNet集成于自主提出的机器人探索与地图预测架构4CNet-E中，通过大量对比实验，与当前最先进的启发式及学习方法在包含不规则障碍物与不平坦地形的环境中，系统评估了地图预测与探索性能。实验结果表明：在不同环境规模、机器人数量、能量预算及通信限制条件下，4CNet-E均能取得统计意义上显著更高的预测精度与区域覆盖率。硬件实验验证了4CNet-E在非结构化室内环境与真实自然室外场景中的适用性与泛化能力。