Social robots can help humans in various aspects of everyday life. However, they usually cannot perform physical operations, such as opening doors or operating elevators. Therefore, the capability of finding a person to help could compensate for this weakness.
A recent paper on arXiv.org proposes to use the Behavior Tree (BT) framework for finding people in an open space. It balances the proactive search and waiting to find a person in the shortest possible time.
Robonaut. Image credit NASA via Pixabay
The BTs are synthesized based on a spatial model of people occurrence rate. The approach has advantages when compared with other methods because it is modular, reusable, and expandable. Therefore, the trees can be expanded to include actions as approaching a person or verbal questions for help. The real-world experiments showed that the suggested model produces suitable predictions of the time until success and finds people in 94% of all cases.
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.
Research paper: Stuede, M., Lerche, T., Petersen, M. A., and Spindeldreier, S., “Behavior-Tree-Based Person Search for Symbiotic Autonomous Mobile Robot Tasks”, 2021. Link: https://arxiv.org/abs/2103.09162