We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person-view images. In order to overcome the need for positioning, we train the sensors to encode and communicate relevant viewpoint information to the mobile robot, whose objective it is to use this information to navigate as efficiently as possible to the target. We overcome the challenge of enabling all the sensors (even those that cannot directly see the target) to predict the direction along the shortest path to the target by implementing a neighborhood-based feature aggregation module using a Graph Neural Network (GNN) architecture. In our experiments, we first demonstrate generalizability to previously unseen environments with various sensor layouts. Our results show that by using communication between the sensors and the robot, we achieve up to 2.0x improvement in SPL (Success weighted by Path Length) when compared to a communication-free baseline. This is done without requiring a global map, positioning data, nor pre-calibration of the sensor network. Second, we perform a zero-shot transfer of our model from simulation to the real world. Laboratory experiments demonstrate the feasibility of our approach in various cluttered environments. Finally, we showcase examples of successful navigation to the target while the sensor network layout is dynamically reconfigured.

翻译：我们考虑在配备视觉传感器的未知环境中，引导移动机器人朝向目标导航的问题，其中机器人及传感器均无法获取全局定位信息，仅能使用第一视角图像。为克服对定位的需求，我们训练传感器对相关视角信息进行编码并与移动机器人通信，机器人利用该信息尽可能高效地导航至目标。通过采用基于图神经网络（GNN）架构的邻域特征聚合模块，我们解决了使所有传感器（包括无法直接观测目标的传感器）均能预测沿最短路径朝向目标方向的挑战。在实验中，我们首先验证了该方法在具有不同传感器布局的陌生环境中的泛化能力。结果表明，通过传感器与机器人之间的通信，相比无通信基线方法，SPL（按路径长度加权的成功率）指标最高可提升2.0倍，且无需依赖全局地图、定位数据或传感器网络的预校准。其次，我们实现了从仿真到真实世界的零样本模型迁移。实验室实验证明了该方法在各类杂乱环境中的可行性。最后，我们展示了在传感器网络布局动态重组情况下成功导航至目标的实例。