In unknown cluttered and dynamic environments such as disaster scenes, mobile robots need to perform target-driven navigation in order to find people or objects of interest, while being solely guided by images of the targets. In this paper, we introduce NavFormer, a novel end-to-end transformer architecture developed for robot target-driven navigation in unknown and dynamic environments. NavFormer leverages the strengths of both 1) transformers for sequential data processing and 2) self-supervised learning (SSL) for visual representation to reason about spatial layouts and to perform collision-avoidance in dynamic settings. The architecture uniquely combines dual-visual encoders consisting of a static encoder for extracting invariant environment features for spatial reasoning, and a general encoder for dynamic obstacle avoidance. The primary robot navigation task is decomposed into two sub-tasks for training: single robot exploration and multi-robot collision avoidance. We perform cross-task training to enable the transfer of learned skills to the complex primary navigation task without the need for task-specific fine-tuning. Simulated experiments demonstrate that NavFormer can effectively navigate a mobile robot in diverse unknown environments, outperforming existing state-of-the-art methods in terms of success rate and success weighted by (normalized inverse) path length. Furthermore, a comprehensive ablation study is performed to evaluate the impact of the main design choices of the structure and training of NavFormer, further validating their effectiveness in the overall system.

翻译：在灾害现场等未知、杂乱且动态变化的环境中，移动机器人需要仅依据目标物体的图像引导，执行目标驱动导航以寻找人员或感兴趣物体。本文提出NavFormer，一种专为未知动态环境中机器人目标驱动导航设计的新型端到端Transformer架构。NavFormer融合了以下两方面的优势：1）Transformer对序列数据的处理能力；2）自监督学习在视觉表征方面的潜力，从而实现对空间布局的推理与动态环境中的避障。该架构创新性地整合了双视觉编码器：静态编码器用于提取不变环境特征以支持空间推理，通用编码器则用于动态避障。我们将机器人主导航任务分解为两个训练子任务：单机器人探索与多机器人避障。通过跨任务训练，使习得技能能够迁移至复杂的主导航任务，而无需任务特定的微调。仿真实验表明，NavFormer能够在多样化的未知环境中有效引导移动机器人导航，在成功率和（归一化逆）路径长度加权成功率方面均优于现有先进方法。此外，本文通过系统的消融实验评估了NavFormer结构与训练中主要设计选择的影响，进一步验证了其在整体系统中的有效性。