Target Reaching Behaviour for Unfreezing the Robot in a Semi-Static and Crowded Environment

TL;DR

This paper presents a novel robot behavior that enables a wheeled humanoid robot to interact with humans and clear its path in crowded environments, addressing the freezing problem in navigation.

Contribution

It introduces a social norm-compliant behavior combining human detection and gesture policies to resolve robot freezing in crowded, semi-static environments.

Findings

The robot successfully detects human hands and arms using YOLOv3.

The gesture policy effectively clears the path by interacting with humans.

The approach improves navigation in crowded settings by reducing freezing incidents.

Abstract

Robot navigation in human semi-static and crowded environments can lead to the freezing problem, where the robot can not move due to the presence of humans standing on its path and no other path is available. Classical approaches of robot navigation do not provide a solution for this problem. In such situations, the robot could interact with the humans in order to clear its path instead of considering them as unanimated obstacles. In this work, we propose a robot behavior for a wheeled humanoid robot that complains with social norms for clearing its path when the robot is frozen due to the presence of humans. The behavior consists of two modules: 1) A detection module, which make use of the Yolo v3 algorithm trained to detect human hands and human arms. 2) A gesture module, which make use of a policy trained in simulation using the Proximal Policy Optimization algorithm. Orchestration of the two models is done using the ROS framework.