RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring
This addresses the problem of flexible and safe navigation for robot assistants in cluttered domestic environments, though it is incremental as it builds on existing DWA and DRL techniques.
The paper tackled person following for domestic robot assistants by introducing a method that uses an omnidirectional platform with a DRL agent for angular velocity control and DWA for linear velocity, achieving competitive advantage over standard differential steering in indoor scenarios.
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.