Target Recovery for Robust Deep Learning-Based Person Following in Mobile Robots: Online Trajectory Prediction

The ability to predict a person's trajectory and recover a target person in the event the target moves out of the field of view of the robot's camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment.

Automated summary

This paper presents an extended work on online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework allows the robot to track and predict the trajectory of a specific person while avoiding obstacles in real-time, demonstrating effectiveness in realistic environments.