论文标题
RL-DWA全向运动计划，以追随家庭援助和监测
RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring
论文作者
论文摘要
机器人助手正在作为高科技解决方案，以支持日常生活中的人们。在国内环境中跟随并协助用户需要灵活的移动性，以便在杂乱无章的空间中安全地移动。我们向人们介绍了一种新方法以寻求帮助和监控。我们的方法学利用了全向机器人平台来分离线性和角速度的计算，并在家庭环境中导航而不会失去辅助人员的跟踪。虽然线性速度是通过常规动态窗口方法（DWA）本地规划师管理的，但我们训练了深入的加固学习（DRL）代理，以预测优化的角速度命令，并保持机器人对用户的方向。我们在各种室内场景中在真实的全向平台上评估了我们的导航系统，与标准的差分转向跟随相比，我们的解决方案的竞争优势。
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.
