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. While prior work in sensor network based navigation uses explicit mapping and planning techniques, and are often aided by external positioning systems, we propose a vision-only based learning approach that leverages a Graph Neural Network (GNN) to encode and communicate relevant viewpoint information to the mobile robot. During navigation, the robot is guided by a model that we train through imitation learning to approximate optimal motion primitives, thereby predicting the effective cost-to-go (to the target). In our experiments, we first demonstrate generalizability to previously unseen environments with various sensor layouts. Simulation results show that by utilizing communication among the sensors and robot, we can achieve a $18.1\%$ improvement in success rate while decreasing path detour mean by $29.3\%$ and variability by $48.4\%$. 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. To this end, we train a `translator' model that translates between {latent encodings of} real and simulated images so that the navigation policy (which is trained entirely in simulation) can be used directly on the real robot, without additional fine-tuning. Physical experiments demonstrate our effectiveness in various cluttered environments.
翻译:我们考虑了在具有视觉传感器的未知环境中将移动机器人引向目标的问题,在这种环境中,机器人和传感器都无法获取全球定位信息,只能使用第一人看图像。虽然以前在传感器网络上的工作使用明确的绘图和规划技术,而且往往得到外部定位系统的帮助,但我们提议了一种基于视觉的学习方法,利用图形神经网络(GNN)将相关观点信息编码和传送到移动机器人。在导航期间,机器人遵循一种模型,我们通过模拟学习来培训,以近似最佳运动原始,从而预测有效的成本到目标。在我们进行的实验中,我们首先以各种传感器布局向先前看不见的环境展示了一般可操作性。模拟结果表明,通过使用传感器和机器人之间的通信,我们可以实现18.1美元的成功率改进,同时将路径的平均值降低29.3 美元,将相关观点信息转换为48.4 美元。这不需要全球地图、定位数据或传感器网络的预校准,从而预测有效的成本环境 。第二,我们用真实的模拟模型进行一个零光版的模拟,我们用真实的模拟将模型转换成真实的模型。