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Review Article | Open Access

A Review of Brain-Inspired Cognition and Navigation Technology for Mobile Robots

Yanan Bai1,2,3Shiliang Shao2,3()Jin Zhang1,2,3Xianzhe Zhao1,2,3Chuxi Fang1,2,3Ting Wang2,3()Yongliang Wang4Hai Zhao1
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
Department of Artificial Intelligence, University of Groningen, Groningen 9747 AG, Netherlands
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Abstract

Brain-inspired navigation technologies combine environmental perception, spatial cognition, and target navigation to create a comprehensive navigation research system. Researchers have used various sensors to gather environmental data and enhance environmental perception using multimodal information fusion. In spatial cognition, a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition map. However, existing models face challenges in achieving high navigation success rate and efficiency. In addition, the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further exploration. On the basis of the brain-inspired navigation process, this paper launched a systematic study on brain-inspired environment perception, brain-inspired spatial cognition, and goal-based navigation in brain-inspired navigation, which provides a new classification of brain-inspired cognition and navigation techniques and a theoretical basis for subsequent experimental studies. In the future, brain-inspired navigation technology should learn from more perfect brain-inspired mechanisms to improve its generalization ability and be simultaneously applied to large-scale distributed intelligent body cluster navigation. The multidisciplinary nature of brain-inspired navigation technology presents challenges, and multidisciplinary scholars must cooperate to promote the development of this technology.

References

1

Honkanen A, Adden A, Da Silva Freitas J, Heinze S. The insect central complex and the neural basis of navigational strategies. J Exp Biol. 2019;222(Suppl_1):jeb188854.

2

O’Keefe J, Dostrovsky J. The hippocampus as a spatial map: Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 1971;34:171–175.

3

Hori E, Nishio Y, Kazui K, Umeno K, Tabuchi E, Sasaki K, Endo S, Ono T, Nishijo H. Place-related neural responses in the monkey hippocampal formation in a virtual space. Hippocampus. 2005;15(8):991–996.

4

Guy M. Neurosurgical recordings reveal cellular networks underlying human spatial navigation. Neurosurgery. 2004;3:1.

5

Thompson LT, Best PJ. Long-term stability of the place-field activity of single units recorded from the dorsal hippocampus of freely behaving rats. Brain Res. 1990;509(2):299–308.

6

Muller R, Kubie J. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J Neurosci. 1987;7(7):1951–1968.

7

Leutgeb S, Leutgeb JK, Barnes CA, Moser EI, McNaughton BL, Moser M-B. Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Science. 2005;309(5734):619–623.

8

Killian NJ, Jutras MJ, Buffalo EA. A map of visual space in the primate entorhinal cortex. Nature. 2012;491(7426):761–764.

9

Jacobs J, Weidemann CT, Miller JF, Solway A, Burke JF, Wei XX, Suthana N, Sperling MR, Sharan AD, Fried I, et al. Direct recordings of grid-like neuronal activity in human spatial navigation. Nat Neurosci. 2013;16(9):1188–1190.

10

Mouritsen H. Long-distance navigation and magnetoreception in migratory animals. Nature. 2018;558:50–59.

11

Towse BW, Barry C, Bush D, Burgess N. Optimal configurations of spatial scale for grid cell firing under noise and uncertainty. Philos Trans R Soc B. 2014;369(1635):20130290.

12

Taube J, Muller R, Ranck J. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J Neurosci. 1990;10(2):420–435.

13

Kim M, Maguire EA. Encoding of 3D head direction information in the human brain. Hippocampus. 2019;29(7):619–629.

14

Taube J, Muller R, Ranck J. Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. J Neurosci. 1990;10(2):436–447.

15

Park S, Lee K, Song H, Cho J, Park S-Y, Yoon E. Low-power, bio-inspired time-stamp-based 2-D optic flow sensor for artificial compound eyes of micro air vehicles. IEEE Sensors J. 2019;19(24):12059–12068.

16

Chiu M-Y, Chen GC, Hsu TH, Liu RS, Lo CC, Tang KT, Chang MF, Hsieh CC. A multimode vision sensor with temporal contrast pixel and column-parallel local binary pattern extraction for dynamic depth sensing using stereo vision. IEEE J Solid State Circuits. 2023;58(10):2767–2777.

17
Shukla R, Routray PK, Tiwari K, LaValle SM, Manivannan M. Monofilament whisker-based mobile robot navigation. Paper presented at: 2021 IEEE World Haptics Conference (WHC); 2021 July 06–09; Montreal, QC, Canada.
18
Weerakkodi Mudalige ND, Nazarova E, Babataev I, Babataev P, Fedoseev A, Cabrera MA, Tsetserukou D. DogTouch: CNN-based recognition of surface textures by quadruped robot with high density tactile sensors. Paper presented at: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring); 2022 June 19–22; Helsinki, Finland.
19
Borkar N, Krishnamurthy P, Tzes A, Khorrami F. Autonomous navigation of quadrotors using tactile feedback. Paper presented at: 2023 9th International Conference on Automation, Robotics and Applications (ICARA); 2023 February 10–12; Abu Dhabi, United Arab Emirates.
20

Weerakoon K, Sathyamoorthy AJ, Liang J, Guan T, Patel U, Manocha D. GrASPE: Graph based multimodal fusion for robot navigation in outdoor environments. IEEE Robot Autom Lett. 2023;8(12):8090–8097.

21
Linegar C, Churchill W, Newman P. Made to measure: Bespoke landmarks for 24-hour, all-weather localisation with a camera. Paper presented at: 2016 IEEE International Conference on Robotics and Automation (ICRA); 2016 May 16–21; Stockholm, Sweden.
22
Chengqing W, Chenning L, Haowei X. An improved visual indoor navigation method based on fully convolutional neural network. Paper presented at: 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC); 2020 August 21–24; Macau, China.
23
Sun C, Qiao N, Sun J. Robust feature matching based on adaptive ORB for vision-based robot navigation. Paper presented at: 2021 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC); 2021 May 28–30; Nanchang, China.
24

Wang H, Huang L, Du C, Li D, Wang B, He H. Neural encoding for human visual cortex with deep neural networks learning ‘what’ and ‘where. IEEE Trans Cogn Dev Syst. 2021;13(4):827–840.

25
Plebe A, Kooij JFP, Pietro Rosati Papini G, Da Lio M. Occupancy grid mapping with cognitive plausibility for autonomous driving applications. Paper presented at: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW); 2021 October 11–17; Montreal, BC, Canada.
26

Fazeli N, Oller M, Wu J, Wu Z, Tenenbaum JB, Rodriguez A. See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion. Sci Robot. 2019;4(26):eaav3123.

27

Liu X, Chen L, Jiao Z, Yu F, Lu X, Liu Z, Ruan Y. A neuro-inspired positioning system integrating MEMS sensors and DTMB signals. IEEE Trans Broadcast. 2023;69(3):823–831.

28
Li W, Wu D, Dai C, Wu D. A position representation method based on the localization mechanism of rat hippocampus. Paper presented at: 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD); 2022 May 27–30; Chengdu, China.
29
Li X, Guo K, Jia T. Visual perception and navigation of security robot based on deep learning. Paper presented at: 2020 IEEE International Conference on Mechatronics and Automation (ICMA); 2020 October 13–16; Beijing, China.
30
Walch F, Hazirbas C, Leal-Taixé L, Sattler T, Hilsenbeck S, Cremers D. Image-based localization using LSTMs for structured feature correlation. Paper presented at: 2017 IEEE International Conference on Computer Vision (ICCV); 2017 October 22–29; Venice, Italy.
31
Yang C, Liu Y, Zell A. RCPNet: Deep-learning based relative camera pose estimation for UAVs. Paper presented at: 2020 International Conference on Unmanned Aircraft Systems (ICUAS); 2020 October 1–4; Athens, Greece.
32
Sindhu S, Saravanan M. Part-based convolutional neural network and dual interactive Wasserstein generative adversarial networks for land mark detection and localization of autonomous robots in outdoor environment. Paper presented at: 2022 1st International Conference on Computational Science and Technology (ICCST); 2022 November 9–10; Chennai, India.
33
Zaman A, Yangyu F, Ayub MS, Guoyun L, Shiva L. Brain inspired keypoint matching for 3D scene reconstruction. Paper presented at: 2022 8th international conference on virtual reality (ICVR); 2022 May 26–28; Nanjing, China.
34
Mai G, Janowicz K, Yan B. Multi-scale representation learning for spatial feature distributions using grid cells. ArXiv. 2020. https://doi.org/10.48550/arXiv.2003.00824.
35

Zhao D, Zhang Z, Lu H, Cheng S, Si B, Feng X. Learning cognitive map representations for navigation by sensory–motor integration. IEEE Trans Cybern. 2022;52(1):508–521.

36

Li J, Tang H, Yan R. A hybrid loop closure detection method based on brain-inspired models. IEEE Trans Cogn Dev Syst. 2022;14(4):1532–1543.

37

Willshaw DJ, Dayan P, Morris RGM. Memory, modelling and Marr: a commentary on Marr (1971) ‘Simple memory: A theory of archicortex’. Philos Trans R Soc B. 2015;370(1666):20140383.

38

Wu S, Wong KYM, Fung CCA. Continuous attractor neural networks: Candidate of a canonical model for neural information representation [version 1; peer review: 2 approved]. F1000Research. 2016;5(F1000 Faculty Rev):156.

39

Chancán M, Hernandez-Nunez L, Narendra A, Barron AB, Milford M. A hybrid compact neural architecture for visual place recognition. IEEE Rob Autom Lett. 2020;5(2):993–1000.

40

Burak Y, Fiete IR. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol. 2009;5(2):Article e1000291.

41
Gu Y, Zhao X, Dai C, Zhang L. Research on ambiguity calculation method of brain-like grid cell path integration under large-scale conditions. Paper presented at: 2020 International Conference on Robots & Intelligent System (ICRIS); 2020 November 7–8; Sanya, China.
42

Han K, Wu D, Lai L, He J. The self-motion information response model in brain-inspired navigation. IEEE Access. 2020;8:49717–49729.

43
Milford MJ, Wyeth GF, Prasser DP. RatSLAM: A hippocampal model for simultaneous localisation and mapping. Paper presented at: IEEE International Conference on Robotics and Automation, 2004; 2004 April 26–May 1; New Orleans, LA, USA.
44

Li B, Liu Y, Lai L. A bio-inspired 3-D neural compass based on head direction cells. IEEE Access. 2021;9:110753–110761.

45
Guth FA, Silveira L, Amaral M, Botelho S, Drews P. Underwater visual 3D SLAM using a bio-inspired system. Paper presented at: 2013 Symposium on Computing and Automation for Offshore Shipbuilding; 2013 March 14–15; Rio Grande, Brazil.
46

Yoon J-H, Raychowdhury A. NeuroSLAM: A 65-nm 7.25-to-8.79-TOPS/W mixed-signal oscillator-based SLAM accelerator for edge robotics. IEEE J Solid State Circuits. 2021;56(1):66–78.

47

Yang C, Xiong Z, Liu J, Chao L, Chen Y. A path integration approach based on multiscale grid cells for large-scale navigation. IEEE Trans Cogn Dev Syst. 2022;14(3):1009–1020.

48
Joseph T, Fischer T, Milford M. Trajectory tracking via multiscale continuous attractor networks. Paper presented at: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2023 October 1–5; Detroit, MI, USA.
49

Zeng T, Tang F, Ji D, Si B. NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation. Neural Netw. 2020;126:21–35.

50

Zhang Z, Tang F, Li Y, Feng X. Modeling the grid cell activity based on cognitive space transformation. Cogn Neurodynamics. 2023;18:1–17.

51

Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Netw. 1997;10(9):1659–1671.

52
Tang G, Michmizos KP. Gridbot: An autonomous robot controlled by a spiking neural network mimicking the brain’s navigational system. In: Proceedings of the international conference on neuromorphic systems. Association for Computing Machinery; 2018. p. 1–8.
53
Zhang W, Li P. Spike-train level backpropagation for training deep recurrent spiking neural networks. In: Advances in neural information processing systems. MIT Press; 2019. p. 32.
54

Dong M, Huang X, Xu B. Unsupervised speech recognition through spike-timing-dependent plasticity in a convolutional spiking neural network. PLoS One. 2018;13(11):Article e0204596.

55
Safa A. Fusing event-based camera and radar for SLAM using spiking neural networks with continual STDP learning. Paper presented at: 2023 IEEE International Conference on Robotics and Automation (ICRA); 2023 May 29–June 2; London, UK.
56
Huang-Yu Y, Huang HP, Huang YC. Flyintel – A platform for robot navigation based on a brain-inspired spiking neural network. Paper presented at: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS); 2019 May 18–20; Hsinchu, Taiwan.
57
Abubaker BA, Ahmed SR, Guron AT, Fadhil M, Algburi S, Abdulrahman BF. Spiking neural network for enhanced mobile robots’ navigation control. Paper presented at: 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS); 2023 November 23–25; Istanbul, Turkiye.
58
Yang B, Yuan M, Zhang C, Hong C, Pan G, Tang H. Spiking reinforcement learning with memory ability for mapless navigation. Paper presented at: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2023 October 1–5; Detroit, MI, USA.
59

Frémaux N, Sprekeler H, Gerstner W, Sprekeler H, Gerstner W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput Biol. 2013;9(4):Article e1003024.

60
Xu R, Wu Y, Qin X, Zhao P. Population-coded spiking neural network with reinforcement learning for mapless navigation. Paper presented at: 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI); 2022 November 18–21; Nanjing, China.
61
Ramezanlou MT, Azimirad V, Sotubadi SV, Janabi-Sharifi F. Spiking neural controller for autonomous robot navigation in dynamic environments. Paper presented at: 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE); 2020 October 29–30; Mashhad, Iran.
62
Chen K. Differential spatial representations in hippocampal CA1 and subiculum emerge in evolved spiking neural networks. Paper presented at: 2021 International Joint Conference on Neural Networks (IJCNN); 2021 July 18–22; Shenzhen, China.
63
Komer B, Jaworski P, Harbour S, Eliasmith C, DeWolf T. BatSLAM: Neuromorphic spatial reasoning in 3D environments. Paper presented at: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC); 2022 September 18–22; Portsmouth, VA, USA.
64
Watkins-Valls D, Xu J, Waytowich N, Allen P. Learning your way without map or compass: Panoramic target driven visual navigation. Paper presented at: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2020 October 24–January 24; Las Vegas, NV, USA.
65
Brown R, Brna A, Cook J, Park S, Aguilar-Simon M. Uncertainty-driven control for a self-supervised lifelong learning drone. Paper presented at: IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium; 2022 July 17–22; Kuala Lumpur, Malaysia.
66

Edvardsen V. Goal-directed navigation based on path integration and decoding of grid cells in an artificial neural network. Nat Comput. 2019;18(1):13–27.

67
Mizutani A, Tanaka Y, Tamukoh H, Katori Y, Tateno K, Morie T. Brain-inspired neural network navigation system with hippocampus, prefrontal cortex, and amygdala functions. Paper presented at: 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS); 2021 November 16–19; Hualien City, Taiwan.
68
Zhang Y, Feng W, Yang Z, Zhou Z, Zhu Z, Wang W. Visual navigation of mobile robots in complex environments based on distributed deep reinforcement learning. Paper presented at: 2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT); 2022 December 9–11; Changzhou, China.
69
Zhang K, Hu Y, Huang D, Yin Z. Target tracking and path planning of mobile sensor based on deep reinforcement learning. Paper presented at: 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS); 2023 May 12–14; Xiangtan, China.
70
Ma H, Wang S, Zhang S, Ren S, Wang H. Map-less end-to-end navigation of mobile robots via deep reinforcement learning. Paper presented at: 2023 IEEE 13th International Conference on Electronics Information and Emergency Communication (ICEIEC); 2023 July 14–16; Beijing, China.
71
Li Z. A hierarchical autonomous driving framework combining reinforcement learning and imitation learning. Paper presented at: 2021 International Conference on Computer Engineering and Application (ICCEA); 2021 June 25–27; Kunming, China.
72

Rao Z, Wu Y, Yang Z, Zhang W, Lu S, Lu W, Zha ZJ. Visual navigation with multiple goals based on deep reinforcement learning. IEEE Trans Neur Netw Learn Syst. 2021;32(12):5445–5455.

73
Campari T, Lamanna L, Traverso P, Serafini L, Ballan L. Online learning of reusable abstract models for object goal navigation. Paper presented at: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022 June 18–24; New Orleans, LA, USA.
74
Luo H, Fang Y. A scene classification method based on improved incremental brain-like developmental model. Paper presented at: 2023 42nd Chinese Control Conference (CCC); 2023 July 24–26; Tianjin, China.
75
Hu L, Hao K, Cai X, Chen L. A spatial cognitive cells inspired goal-directed navigation model. Paper presented at: 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA); 2019 March 29–31; Dalian, China.
76

Huang X, Deng H, Zhang W, Song R, Li Y. Towards multi-modal perception-based navigation: A deep reinforcement learning method. IEEE Robot Autom Lett. 2021;6(3):4986–4993.

77
Huang X, Chen W, Zhang W, Song R, Cheng J, Li Y. Autonomous multi-view navigation via deep reinforcement learning. Paper presented at: 2021 IEEE International Conference on Robotics and Automation (ICRA); 2021 May 30–June 5; Xi’an, China.
78

Liu D, Lyu Z, Zou Q, Bian X, Cong M, Du Y. Robotic navigation based on experiences and predictive map inspired by spatial cognition. IEEE/ASME Trans Mech. 2022;27(6):4316–4326.

79
Singh J, Dhuheir M, Refaey A, Erbad A, Mohamed A, Guizani M, Navigation and obstacle avoidance system in unknown environment. Paper presented at: 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE); 2020 August 30–September 02; London, ON, Canada.
80
Zhuang G, Bing Z, Huang Y, Huang K, Knoll A. A biologically-inspired simultaneous localization and mapping system based on LiDAR sensor. Paper presented at: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2022 October 23–27; Kyoto, Japan.
81
Aishwarya LT, Panda M. Road boundary detection using 3D-to-2D transformation of LIDAR data and conditional generative adversarial networks. Paper presented at: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT); 2020 July 1–3; Kharagpur, India.
82
Shan Q, Wang W, Guo D, Sun X, Jia L. Improving the ability of robots to navigate through crowded environments safely using deep reinforcement learning. Paper presented at: 2022 International Conference on Advanced Robotics and Mechatronics (ICARM); 2022 July 9–11; China: Guilin.
83
Doukhi O, Kang D, Ryu Y, Lee J, Lee DJ. Learning-based NMPC for agile navigation and obstacle avoidance in unstructured environments. Paper presented at: 2023 23rd International Conference on Control, Automation and Systems (ICCAS); 2023 October 17–20; Yeosu, Republic of Korea.
84
Ruan X, Lin C, Huang J, Li Y. Obstacle avoidance navigation method for robot based on deep reinforcement learning. Paper presented at: 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC); 2022 March 4–6; Chongqing, China.
85
Phueakthong P, Varagul J, Pinrath N. Deep reinforcement learning based mobile robot navigation in unknown environment with continuous action space. Paper presented at: 2022 5th International Conference on Intelligent Autonomous Systems (ICoIAS); 2022 September 23–25; Dalian, China.
86
Nossier E, Ibrahim F, Abdelwahab M, AbdelAziz M. Path planning algorithm for dynamic obstacles based on support vector machines towards autonomous navigation. Paper presented at: 2021 16th International Conference on Computer Engineering and Systems (ICCES); 2021 December 15–16; Cairo, Egypt.
87

Yang S, Tan J, Lei T, Linares-Barranco B. Smart traffic navigation system for fault-tolerant edge computing of internet of vehicle in intelligent transportation gateway. IEEE Trans Intell Transp Syst. 2023;24(11):13011–13022.

88

Xing D, Li J, Zhang T, Xu B. A brain-inspired approach for collision-free movement planning in the small operational space. IEEE Trans Neur Netw Learn Syst. 2022;33(5):2094–2105.

89
Menon A, Natarajan A, Olascoaga LIG, Kim Y, Benedict B, Rabaey JM. On the role of hyperdimensional computing for behavioral prioritization in reactive robot navigation tasks. Paper presented at: 2022 International Conference on Robotics and Automation (ICRA); 2022 May 23–27; Philadelphia, PA, USA.
90

Li M, Wei R, Zhang Z, Zhang P, Xu G, Liao W. CVT-based asynchronous BCI for brain-controlled robot navigation. Cyborg Bionic Syst. 2023;4:0024.

Cyborg and Bionic Systems
Article number: 0128
Cite this article:
Bai Y, Shao S, Zhang J, et al. A Review of Brain-Inspired Cognition and Navigation Technology for Mobile Robots. Cyborg and Bionic Systems, 2024, 5: 0128. https://doi.org/10.34133/cbsystems.0128
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