A biological-inspired episodic cognitive map building framework for mobile robot navigation

Liu, Dong; Cong, Ming; Zou, Qiang; Du, Yu

doi:10.1177/1729881417705922

Cited by 12 publications

(6 citation statements)

References 26 publications

(27 reference statements)

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“…Figure 11 b is the experience map of the corresponding experimental scene output by the system. Figure 11 c shows the path from starting point A to end point B planned by path planning and based on episodic memory, as proposed by [ 39 ]. Figure 11 d shows the optimal path planned by the system after integrating the connectivity network.…”

Section: Experiments With Connectivity Networkmentioning

confidence: 99%

Bionic Path Planning Fusing Episodic Memory Based on RatSLAM

Shu

et al. 2023

Biomimetics

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Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.

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Section: Experiments With Connectivity Networkmentioning

confidence: 99%

Bionic Path Planning Fusing Episodic Memory Based on RatSLAM

Shu

et al. 2023

Biomimetics

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“…The CAN cognitive model is different from these proposing methods. 25,26 Head direction cell. Head direction cell is important for the movement of the animal.…”

Section: Can Cognitive Modelmentioning

confidence: 99%

An improved bioinspired cognitive map-building system based on episodic memory recognition

Wang

Xiaojun

et al. 2020

International Journal of Advanced Robotic Systems

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Before the cognitive map is generated through the fire of the rodent hippocampal spatial cells, mammals can obtain the outside knowledge through the visual information, which comes from the eyeball to the brain. The information is encoded and transferred to the two regions of the brain based on the fact of biophysiological research, which are known as “what” loop and “where” loop. In this article, we simulate an episodic memory recognition unit consisting of the integration of two-loop information, which is applied to building the accurate bioinspired spatial cognitive map of real environments. We employ the visual bag of word algorithm based on oriented Feature from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features feature to build the “what” loop and the hippocampal spatial cells cognitive model, which comes from the front-end visual information input system to build the “where” loop. At the same time, the environmental cognitive map is a topological map containing information about place cell competition firing rate, oriented Feature from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features feature descriptor, similarity of image retrieval, and relative location of cognitive map nodes. The simulation experiments and physical experiments in a mobile robot platform have been done to verify the environmental adaptability and robustness of the algorithm. This proposing algorithm would provide a foundation for further research on bioinspired navigation of robots.

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“…31 Such multi-robot systems may be modeled as MDPs. [32][33][34][35] Cizelj et al 36 present an approach to control a vehicle in a hostile environment while considering static obstacles as well as moving adversaries against whom the vehicle must protect itself from collisions. They adopt the PCTL-based control synthesis approach to plan the mission for the vehicle and use an off-the-shelf PCTL model-checking tool PRISM for this purpose.…”

Section: Handling Stochastic Eventsmentioning

confidence: 99%

Modeling and verification of contingency resolution strategies for multi-robot missions using temporal logic

Shriyam

Gupta

2019

International Journal of Advanced Robotic Systems

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This article presents an approach for assessing contingency resolution strategies using temporal logic. We present a framework for nominal mission modeling, then specifying contingency resolution strategies and evaluating their effectiveness for the mission. Our approach focuses on leveraging the use of model checkers to the domain of multi-robot missions to assess the adequacy of contingency resolution strategies that minimize the adverse effects of contingencies on the mission execution. We consider missions with deterministic as well as probabilistic transitions. We demonstrate our approach using two case studies. We consider the escorting of a ship in a port where multiple contingencies may occur concurrently and assess the adequacy of the proposed contingency resolution strategies. We also consider a manufacturing scenario where multiple assembly stations collaborate to create a product. In this case, assembly operations may fail, and human intervention is needed to complete the assembly process. We investigate several different strategies and assess their effectiveness based on mission characteristics.

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A biological-inspired episodic cognitive map building framework for mobile robot navigation

Cited by 12 publications

References 26 publications

Bionic Path Planning Fusing Episodic Memory Based on RatSLAM

Bionic Path Planning Fusing Episodic Memory Based on RatSLAM

An improved bioinspired cognitive map-building system based on episodic memory recognition

Modeling and verification of contingency resolution strategies for multi-robot missions using temporal logic

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