COROS: A Multi-Agent Software Architecture for Cooperative and Autonomous Service Robots

IEICE Trans. Inf. & Syst.

2018

Certain open issues challenge the software engineering of autonomous robot software (ARS). One issue is to provide enabling software technologies to support autonomous and rational behaviours of robots operating in an open environment, and another issue is the development of an effective engineering approach to manage the complexity of ARS to simplify the development, deployment and evolution of ARS. We introduce the software framework AutoRobot to address these issues. This software provides abstraction and a model of accompanying behaviours to formulate the behaviour patterns of autonomous robots and enrich the coherence between task behaviours and observation behaviours, thereby improving the capabilities of obtaining and using the feedback regarding the changes. A dual-loop control model is presented to support flexible interactions among the control activities to support continuous adjustments of the robot's behaviours. A multi-agent software architecture is proposed to encapsulate the fundamental software components. Unlike most existing research, in AutoRobot, the ARS is designed as a multi-agent system in which the software agents interact and cooperate with each other to accomplish the robot's task. AutoRobot provides reusable software packages to support the development of ARS and infrastructure integrated with ROS to support the decentralized deployment and running of ARS. We develop an ARS sample to illustrate how to use the framework and validate its effectiveness.

“…AutoRobot is a type of agent-based framework. VOMAS (Virtual Operator Multi-Agent System) [27] and COROS [13] are common agent-based frameworks for robotic software.…”

Section: Related Work and Discussionmentioning

confidence: 99%

<i>AutoRobot</i>: A Multi-Agent Software Framework for Autonomous Robots

Liu

IEICE Trans. Inf. & Syst.

2018

“…The COROS developed by Koubâa et al 26 is a multiagent software architecture for cooperative robots in which each of the agents represents a robot machine or a monitoring/control workstation. The COROS system consists of five conceptual subsystems, that is, communication, ROS interaction layer, robot control, application and knowledge bases.…”

Section: Implementation Approachmentioning

confidence: 99%

Towards a hybrid software architecture and multi-agent approach for autonomous robot software

International Journal of Advanced Robotic Systems

et al. 2017

To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.

“…Most of the existing research focuses on the cooperation in a multi-agent (or multi-robot) system that consists of a group of UAVs or UGVs. For example, Koubâa et al introduced COROS [9], a high-level conceptual architecture for…”

Section: Introductionmentioning

confidence: 99%

IADRL: Imitation Augmented Deep Reinforcement Learning Enabled UGV-UAV Coalition for Tasking in Complex Environments

Zhang

et al. 2020

IEEE Access

Recent developments in Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have made them highly useful for various tasks. However, they both have their respective constraints that make them incapable of completing intricate tasks alone in many scenarios. For example, a UGV is unable to reach high places, while a UAV is limited by its power supply and payload capacity. In this paper, we propose an Imitation Augmented Deep Reinforcement Learning (IADRL) model that enables a UGV and UAV to form a coalition that is complementary and cooperative for completing tasks that they are incapable of achieving alone. IADRL learns the underlying complementary behaviors of UGVs and UAVs from a demonstration dataset that is collected from some simple scenarios with non-optimized strategies. Based on observations from the UGV and UAV, IADRL provides an optimized policy for the UGV-UAV coalition to work in an complementary way while minimizing the cost. We evaluate the IADRL approach in an visual game-based simulation platform, and conduct experiments that show how it effectively enables the coalition to cooperatively and cost-effectively accomplish tasks. INDEX TERMS Unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), coalition, deep reinforcement learning (DRL), imitation learning. JIAN ZHANG (Member, IEEE) received the B.Sc. and M.Sc. degrees in applied physics from Sichuan University, Chengdu, China, in 2001 and 2008, respectively, and the Ph.D. degree in electrical and computer engineering from Auburn University, Auburn, AL, USA, in 2016. He is currently an Assistant Research Professor with the RFID Laboratory, Auburn University. His main research interests include RFID technologies and applications, the Internet of Things, indoor localization, UAV, and collaborative robotics. His work focuses on improving the efficiency of supply chain management for industry and business.