A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment
Department of Computer Science and Engineering, NIT Patna, Bihar 800005, India
Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, Akbarpur, Uttar Pradesh 224122, India
International Institute of Information Technology (IIIT), Naya Raipur, Chhattisgarh 493661, India
School of Information Technology and Engineering, Vellore Institute of Technology, Tamil Nadu 632014, India
Department of Mechatronics Engineering, Chungnam National University, Daejeon 34134, Korea
Department of Industrial & Systems Engineering, Dongguk University, Seoul 04620, Korea
School of Computer, Information and Communication Engineering, Kunsan National University, Gunsan 54150, Korea
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5147; https://doi.org/10.3390/app10155147
Received: 19 June 2020 / Revised: 19 July 2020 / Accepted: 23 July 2020 / Published: 27 July 2020
(This article belongs to the Special Issue Cognitive Robotics)
Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To overcome this problem, in this work, an agent is computationally devised that mimics the grid and place neuron functionality to learn cognitive maps from the input spatial data of an environment or an object. A novel quadrant-based approach is proposed to model the behavior of the grid neuron, which, like the real grid neuron, is capable of generating periodic hexagonal grid-like output patterns from the input body movement. Furthermore, a cognitive map formation and their learning mechanism are proposed using the place–grid neuron interaction system, which is meant for making predictions of environmental sensations from the body movement. A place sequence learning system is also introduced, which is like an episodic memory of a trip that is forgettable based on their usage frequency and helps in reducing the accumulation of error during a visit to distant places. The model has been deployed and validated in two different spatial data learning applications, one being the 2D object detection by touch, and another is the navigation in an environment. The result analysis shows that the proposed model is significantly associated with the expected outcomes.