RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring

In recent years, population ageing and pandemics have been demonstrated to cause isolation of older adults in their houses, generating the need for a reliable assistive figure. Service robotics recently emerged as high-tech support to the problem, providing a series of aid functionality to satisfy daily indoor assistance. Robotic solutions take care of interactive social aspects [1] or monitoring the health status of the user [2, 3]. Domestic environments are often very demanding for autonomous navigation systems due to the variety of complex and dynamic obstacles they can feature. To this end, the robot platform shall provide extreme flexibility and effective mobility to handle narrow passages thought for humans. Moreover, in order to properly assist the user, the platform should be able to follow them within this environment. Person following [4, 5] is the first step to enable any visual or vocal interaction with the user while monitoring its condition to intervene earlier in the case of anomalous events. Person following systems are often based on naive visual-control strategy, directly coupling the generation of heuristic commands for the robot with the person coordinate in the image [6]. Deep Reinforcement Learning (DRL) agents recently demonstrated significant autonomy and flexibility boost in robotic solutions.