Behavior-Tree-Based Person Search for Symbiotic Autonomous Mobile Robot Tasks
This work addresses the challenge of human-robot collaboration in dynamic environments for mobile robots, though it appears incremental as it builds on existing Behavior Tree frameworks with specific optimizations.
The paper tackles the problem of enabling a mobile social robot to find a person for assistance in tasks like opening doors or operating elevators, using a Behavior Tree framework decomposed as a Discrete Time Markov Chain to estimate success probabilities. In real-world experiments with 588 test runs, the method demonstrated superior success rates and faster durations compared to other approaches.
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.