In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (~300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

翻译：本文研究移动机器人在导航中定位毫米波无线信号的问题。利用信号的方向性特性，我们提出了一种能够高效导航于杂乱室内环境的估计与路径规划算法。针对信号在视距传播或反射后接收的两种情况，我们构建了用于发射源位置估计的扩展卡尔曼滤波器。进一步，我们提出基于置信空间动力学规划运动轨迹，以最小化位置估计的不确定性。相关非线性优化问题通过最先进的约束iLQR求解器解决。特别地，我们提出了一种能够处理约300个障碍物且计算时间合理的算法。通过大量仿真验证了该方法，结果表明我们的估计器有助于提高导航成功率，而通过降低估计不确定性的规划策略能够提升整体任务完成速度。