TY - JOUR
T1 - ICACIA
T2 - An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models
AU - Karn, Arodh Lal
AU - Sengan, Sudhakar
AU - Kotecha, Ketan
AU - Pustokhina, Irina V.
AU - Pustokhin, Denis A.
AU - Subramaniyaswamy, V.
AU - Buddhi, Dharam
N1 - Funding Information:
Dr. Ketan Kotecha is currently an Administrator and a Teacher of deep learning. His research interests include artificial intelligence, computer algorithms, machine learning, and deep learning. He has expertise and experience in cutting-edge research and projects in A.I. and deep learning for the last 25 years. He has published more than 100 widely in several excellent peer-reviewed journals on various topics ranging from cutting-edge A.I., education policies, teaching–learning practices, and A.I. for all. He was a recipient of the two SPARC projects worth INR 166 lakhs from MHRD Government of India in A.I. in collaboration with Arizona State University, USA, and The University of Queensland, Australia. He was also a recipient of numerous prestigious awards, like the Erasmus+ Faculty Mobility Grant to Poland, the DUO-India Professors Fellowship for research in responsible A.I. in collaboration with Brunel University, U.K., the LEAP Grant at Cambridge University, U.K., the UKIERI Grant with Aston University, U.K., and a Grant from the Royal Academy of Engineering, U.K., under Newton Bhabha Fund. He has published three patents and delivered keynote speeches at various national and international forums, including at the Machine Intelligence Laboratory, USA, IIT Bombay under the World Bank Project, the International Indian Science Festival organized by the Department of Science and Technology, Government of India, and many more. He is an Associate Editor of the IEEE Access journal.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/11
Y1 - 2022/11
N2 - Most of the world’s most advanced defense technologies are robots, and the defence industry is slowly moving toward including AI in the military robots they build. For these smart robots to make their own decisions about where to go and what to do, they need to be limited by several algorithms that run continuously and at the same time. Autonomy is the range of automated systems that can be adapted to a specific mission, residual risk, and level of team cohesion between humans and robots. Self-driving robotic systems should be collaborative, which means they should be able to interact actively with humans in a shared space or in proximity to humans and robots. Human–Robot Collaboration (HRC) works better when these COBOTs are aware of their surroundings. Mobile Robot (MR) teams whose perceptual and cognitive abilities are very well developed can help a lot with context awareness. To work well with humans, these robots should know what is going on with their human and other robot teammates so they can make decisions on their own. Also, robots should be able to share information about their surroundings so that humans can benefit from a better understanding of the situation. At the same time, humans should be able to see what the robots are doing. In this paper, we propose a knowledge-based framework for humans and robots to work together to understand the context of Defense missions. An ontological model of contexts for missions, agents, and situations; a knowledge base comprising all the tools necessary for a sort of situation; and an efficient and reliable method of collaborative learning are some of its main contributions. The framework works well in terms of how long it takes for people to talk to each other. As the team continues to expand, it can also easily manage communication challenges and a widely differing event frequency range.
AB - Most of the world’s most advanced defense technologies are robots, and the defence industry is slowly moving toward including AI in the military robots they build. For these smart robots to make their own decisions about where to go and what to do, they need to be limited by several algorithms that run continuously and at the same time. Autonomy is the range of automated systems that can be adapted to a specific mission, residual risk, and level of team cohesion between humans and robots. Self-driving robotic systems should be collaborative, which means they should be able to interact actively with humans in a shared space or in proximity to humans and robots. Human–Robot Collaboration (HRC) works better when these COBOTs are aware of their surroundings. Mobile Robot (MR) teams whose perceptual and cognitive abilities are very well developed can help a lot with context awareness. To work well with humans, these robots should know what is going on with their human and other robot teammates so they can make decisions on their own. Also, robots should be able to share information about their surroundings so that humans can benefit from a better understanding of the situation. At the same time, humans should be able to see what the robots are doing. In this paper, we propose a knowledge-based framework for humans and robots to work together to understand the context of Defense missions. An ontological model of contexts for missions, agents, and situations; a knowledge base comprising all the tools necessary for a sort of situation; and an efficient and reliable method of collaborative learning are some of its main contributions. The framework works well in terms of how long it takes for people to talk to each other. As the team continues to expand, it can also easily manage communication challenges and a widely differing event frequency range.
KW - Communication system
KW - Contextual Intelligence
KW - Deep Learning
KW - Defense industry
KW - Human–Robot Collaboration
KW - Military agents
KW - Mobile Robotic Systems
KW - Ontology
UR - http://www.scopus.com/inward/record.url?scp=85136531314&partnerID=8YFLogxK
U2 - 10.1016/j.robot.2022.104234
DO - 10.1016/j.robot.2022.104234
M3 - Article
AN - SCOPUS:85136531314
SN - 0921-8890
VL - 157
JO - Robotics and Autonomous Systems
JF - Robotics and Autonomous Systems
M1 - 104234
ER -