Behavior-Tree-Based Person Search for Symbiotic Autonomous Mobile Robot Tasks

TL;DR

This paper introduces a behavior-tree-based approach for a mobile robot to search for people in complex tasks, estimating success probabilities and outperforming other methods in real-world tests.

Contribution

The paper presents a novel behavior tree framework that models person search as a Markov process, improving success rates and efficiency in real-world scenarios.

Findings

Higher success rate than other approaches

Faster time to find a person

Effective in real-world university building tests

Abstract

We consider the problem of people search by a mobile social robot in case of a situation that cannot be solved by the robot alone. Examples are physically opening a closed door or operating an elevator. Based on the Behavior Tree framework, we create a modular and easily extendable action sequence with the goal of finding a person to assist the robot. By decomposing the Behavior Tree as a Discrete Time Markov Chain, we obtain an estimate of the probability and rate of success of the options for action, especially where the robot should wait or search for people.In a real-world experiment, the presented method is compared with other common approaches in a total of 588 test runs over the course of one week, starting at two different locations in a university building. We show our method to be superior to other approaches in terms of success rate and duration until a finding person and returning to the start location.