Abstract
This article proposes a self-learning method of robotic experience for building episodic cognitive map using biologically inspired episodic memory. The episodic cognitive map is used for robot navigation under uncertainty. Two main challenges which include high computational complexity and perceptual aliasing are addressed. The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron activation and stimulation mechanism. Episodic memory self-learning model and algorithm are presented for building the episodic cognitive map based on episodic memory-driving Markov decision process. Uncertain information is considered to improve mapping performance. The presented method can realize robotic memory real-time storage, incremental accumulation, integration and updating. Based on the episodic cognitive map, the predicted episodic trajectory can simply be computed by activation spreading of state neurons. The experimental results for a mobile...
Highlights
Building an integrated mobile robot that can navigate freely and deliberately in an indoor environment is a challenging task.[1]
We provide a cognitive map building method for robot navigation under uncertainty using biologically inspired episodic memory
The proposed episodic memory-driving Markov decision process (EM-MDP) framework can avoid the problem of curse of dimensionality and perceptual aliasing in partially observable Markov decision process (POMDP)
Summary
Building an integrated mobile robot that can navigate freely and deliberately in an indoor environment is a challenging task.[1] The robot needs to be integrated with several functional mechanisms, such as scene understanding, mapping, localization and path planning. Spatial cognition is the basic ability of mammals to perform navigation tasks. Researchers have been investigating how animals perceive space and navigate in environment. Cognitive map,[2,3] a map-like representation, which represents the spatial relationship among salient landmarks of an environment, is developed to solve navigation problems. Inspired by the navigation ability of humans or animals, the studies,[4,5,6] which are related to how mammals perform mapping, localization and navigation, have gained extraordinary interests from the robotics community
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