Situational Graphs for Robotic First Responders: an application to dismantling drug labs

TL;DR

This paper introduces a novel environmental representation called the Behavior-Oriented Situational Graph, which integrates perception and actionable knowledge to enhance autonomous and semi-autonomous robot inspection in hazardous environments like drug labs.

Contribution

The paper presents a new graph-based environmental model that improves planning and situational understanding for robots in safety-critical tasks involving drug lab dismantling.

Findings

Effective in real-world drug lab scenarios

Facilitates faster and easier mission planning

Enables seamless transition between autonomy levels

Abstract

In this work, we support experts in the safety domain with safer dismantling of drug labs, by deploying robots for the initial inspection. Being able to act on the discovered environment is key to enabling this (semi-)autonomous inspection, e.g. to open doors or take a closer at suspicious items. Our approach addresses this with a novel environmental representation, the Behavior-Oriented Situational Graph, where we extend on the classical situational graph by merging a perception-driven backbone with prior actionable knowledge via a situational affordance schema. Linking situations to robot behaviors facilitates both autonomous mission planning and situational understanding of the operator. Planning over the graph is easier and faster, since it directly incorporates actionable information, which is critical for online mission systems. Moreover, the representation allows the human operator to seamlessly transition between different levels of autonomy of the robot, from remote control to behavior execution to full autonomous exploration. We test the effectiveness of our approach in a real-world drug lab scenario at a Dutch police training facility using a mobile Spot robot and use the results to iterate on the system design.