A Hybrid System of Hierarchical Planning of Behaviour Selection Networks for Mobile Robot Control

Abstract

An office delivery robot receives a large amount of sensory data and there is uncertainty in its action outcomes. The robot should not only accomplish its goals using environmental information, but also consider various exceptions simultaneously. In this paper, we propose a hybrid system using hierarchical planning of modular behaviour selection networks to generate autonomous behaviour in the office delivery robot. Behaviour selection networks, one of the well-known behaviour-based methods suitable for goal-oriented tasks, are made up of several smaller behaviour modules. Planning is attached to the construct and adjust sequences of the modules by considering the sub-goals, the priority in each task and the user feedback. This helps the robot to quickly react in dynamic situations as well as achieve global goals efficiently. The proposed system is verified with both the Webot simulator and a Khepera II robot that runs in a real office environment carrying out delivery tasks. Experimental results have shown that a robot can achieve goals and generate module sequences successfully even in unpredictable situations. Additionally, the proposed planning method reduced the elapsed time during tasks by 17.5% since it adjusts the behaviour module sequences more effectively.

Towards adaptive multi-robot systems: self-organization and self-adaptation

“…A more recent hybrid approach (Lee & Cho, 2014) models each high-level task as an independent behaviour network that is selected by a planner. This architecture is strongly goal-directed, but loses flexibility for dynamic adaptation since behaviours of different networks cannot be combined with each other.…”

“…The support of dynamic state transitions as well as relying on a reactive agent model provides a good foundation for the future integration of self-adaptation and self-organization capabilities in a multi-robot setting. The presented literature includes many fruitful ideas (Maes, 1989; Hertzberg et al , 1998; Jung, 1998; Allgeuer & Behnke, 2013; Lee & Cho, 2014), even though they are not yet integrated into one solution, and even though not all of them are available for the ROS community.…”

“…Behnke, 2013; Lee & Cho, 2014), even though they are not yet integrated into one solution, and even though not all of them are available for the ROS community.…”

Towards Sustainable Smart Homes by a Hierarchical Hybrid Architecture of an Intelligent Agent

“…Tong [18] Web service × Shen et al [19] Grid service × George et al [20] Patient monitoring × Yang et al [21] Vehicle collision avoidance × Dillienbourg et al [22] Tutoring service Lee et al [23] Delivery service…”

Arquitecturas Centralizadas de Coordinación. Extrapolación del Fútbol de Robots al Control de Modo de Operaciones de Micro-Redes

“…Como se pudo apreciar en los capítulos anteriores, distintos modelos computacionales y arquitecturas jerárquicas han sido utilizadas en sistemas multi-agente, por ejemplo utilizando modelos jerárquicos para la selección de comportamientos para el control de robots móviles [144]. Múltiples Máquinas de Estado Finito son usadas para tele-operación en sistemas multi-robot [145], o arquitecturas jerárquicas son usadas en sistemas de control de tracción en robots móviles [146].…”