Robot assistants are emerging as high-tech solutions to support people in
everyday life. Following and assisting the user in the domestic environment
requires flexible mobility to safely move in cluttered spaces. We introduce a
new approach to person following for assistance and monitoring. Our methodology
exploits an omnidirectional robotic platform to detach the computation of
linear and angular velocities and navigate within the domestic environment
without losing track of the assisted person. While linear velocities are
managed by a conventional Dynamic Window Approach (DWA) local planner, we
trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular
velocities commands and maintain the orientation of the robot towards the user.
We evaluate our navigation system on a real omnidirectional platform in various
indoor scenarios, demonstrating the competitive advantage of our solution
compared to a standard differential steering following.

介绍一种基于全向机器人平台的人员跟随新方法，使用传统的动态窗口法来管理线速度，训练一个深度强化学习代理来预测优化的角速度指令，以保持机器人朝向用户，实现在室内场景下与标准差分转向跟随相比的竞争优势。