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

TL;DR

This paper presents a novel omnidirectional robot navigation system for person following in domestic environments, combining traditional planning with deep reinforcement learning to improve tracking and maneuverability.

Contribution

The study introduces a hybrid approach that separates linear and angular velocity control, using DRL for angular commands, enhancing flexibility and robustness in cluttered indoor spaces.

Findings

The system effectively maintains person tracking in complex indoor scenarios.

It outperforms standard differential steering methods in navigation tasks.

The approach demonstrates real-world applicability on an omnidirectional robot platform.

Abstract

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.