A multi-agent framework for cloud-based management of collaborative robots. (10th July 2018)

• Record Type: Journal Article
• Authors: Samad, Tooba; Iqbal, Sohail; Malik, Asad Waqar; Arif, Omar; Bloodsworth, Peter
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 4(2018:Jul./Aug.)
• Publication Date: 2018-07-10
• Subjects: Multi-agent systems -- cloud computing -- quadcopters -- disaster management; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418785073
• Languages: English
• ISSNs: 1729-8806

Abstract

This article presents a cloud-based multi-agent architecture for the intelligent management of aerial robots in a disaster response situation. In a disaster scenario, a team of highly maneuverable quadcopters is deployed to carry out surveillance and decision support in disaster-affected areas. In Pakistan, such events usually result from sudden unpredictable calamities such as earthquakes. The aim of this work is to develop a robust mechanism to autonomously manage and react to sensory inputs received in soft real time from an unstructured environment. The immediate goal is to locate the maximum number of trapped, injured people within a large area, and help first responders plan rescue activities accordingly. To evaluate the proposed framework, a number of simulations are carried out using GAMA platform to emulate a disaster environment. Subsequently, algorithms are developed to survey an affected geographical area through the use of small flight drones. The key challenges in this work are related to the combination of the domains of multi-agent technology, robotics, and cloud computing for effectively bridging the cyber world with the physical world. Therefore, the proposed work demonstrates the effective use of a limited number of drones to capture inputs from a disaster situation in the physical world, and such inputs are used for timely planning of rescue efforts. The results of fixed resource assignment are compared with the proposed reactive assignment strategy, and it clearly shows a significant improvement in terms of resource usage compared to traditional approach.