Abstract
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.
Highlights
In robotics, the robust localization on an object is very crucial in the perspective of physical tasks.in navigation, the localization and path integration is really a big issue over which several efforts have taken place for more than 20 years [1,2,3]
The task of a place neuron is to learn a unique location of a cognitive map in terms of code as it is activated in a single location in an environment [15,21]
We different numbers of smaller and larger grid neurons that are defined as a value, which is a ratio of the checked different numbers of smaller and larger grid neurons that are defined as a value, which is a number of larger grid spacing grid neurons to the total number of grid neurons
Summary
Rahul Shrivastava 1 , Prabhat Kumar 1 , Sudhakar Tripathi 2 , Vivek Tiwari 3 , Dharmendra Singh Rajput 4 , Thippa Reddy Gadekallu 4 , Bhivraj Suthar 5 , Saurabh Singh 6, *.
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