ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models

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.