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KRIOTA : A framework for Knowledge-management of dynamic Reference Information and Optimal Task Assignment in hybrid edge–cloud environments to support situation-aware robot-assisted operations
Chauhan, Muhammad Aufeef Babar, Muhammed Ali Shen, Haifeng
Chauhan, Muhammad Aufeef
Babar, Muhammed Ali
Shen, Haifeng
Abstract
Enabling an autonomous robotic system (ARS) to be aware of its operating environment can equip the system to deal with unknown and uncertain situations. While several conceptual models have been proposed to establish the fundamental concepts of situational awareness, it remains a challenge to make an ARS situation aware, in particular using a combination of low-cost resource-constraint robots at the tactical edge and powerful remote cloud nodes. This paper proposes a dynamic reference information (DRI) based knowledge management and optimal task assignment framework that manages knowledge extracted from DRI to assess the current situation as per given mission objectives and assigns tasks to different computing nodes, which include a combination of edge robots, edge computing nodes and cloud-hosted services. The proposed framework is referred to as KRIOTA. The framework has been designed using an architecture-centric approach. We have designed ontologies to classify and structure different elements of DRI hierarchically and associate the processing components of an ARS with the DRI. We have devised algorithms for the ARS to optimally assign tasks to relevant processing components on robots, edge computing nodes and cloud-hosted services for adaptive behaviour. We have evaluated the framework by demonstrating its implementation in a testbed named RoboPatrol. We have also demonstrated the performance, effectiveness and feasibility of the KRIOTA framework.
Keywords
Dynamic Reference Information (DRI), Autonomous robotic system (ARS), dynamic tasks allocation, edge–cloud environment, knowledge management
Date
2024
Type
Journal article
Journal
Future Generation Computer Systems
Book
Volume
160
Issue
Page Range
489-504
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled