Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

Qiao, Hong; Wu, Yaxiong; Zhong, Shanlin; Yin, Peng; Chen, Jia-Hao

doi:10.1007/s11633-022-1390-8

Cited by 50 publications

(19 citation statements)

References 127 publications

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“…We believe that our work offers insights into how neuromorphic techniques can improve the capabilities of robots. Future work will focus on integrating NeuroGPR into the next generation of fully neuromorphic Simultaneous Localization and Mapping (SLAM) and navigation systems for brain-inspired robotics ( 60 ), which will allow autonomous robots to operate in natural environments with high robustness, high computing efficiency, and low power consumption.…”

Section: Discussionmentioning

confidence: 99%

Brain-inspired multimodal hybrid neural network for robot place recognition

et al. 2023

Sci. Robot.

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Place recognition is an essential spatial intelligence capability for robots to understand and navigate the world. However, recognizing places in natural environments remains a challenging task for robots because of resource limitations and changing environments. In contrast, humans and animals can robustly and efficiently recognize hundreds of thousands of places in different conditions. Here, we report a brain-inspired general place recognition system, dubbed NeuroGPR, that enables robots to recognize places by mimicking the neural mechanism of multimodal sensing, encoding, and computing through a continuum of space and time. Our system consists of a multimodal hybrid neural network (MHNN) that encodes and integrates multimodal cues from both conventional and neuromorphic sensors. Specifically, to encode different sensory cues, we built various neural networks of spatial view cells, place cells, head direction cells, and time cells. To integrate these cues, we designed a multiscale liquid state machine that can process and fuse multimodal information effectively and asynchronously using diverse neuronal dynamics and bioinspired inhibitory circuits. We deployed the MHNN on Tianjic, a hybrid neuromorphic chip, and integrated it into a quadruped robot. Our results show that NeuroGPR achieves better performance compared with conventional and existing biologically inspired approaches, exhibiting robustness to diverse environmental uncertainty, including perceptual aliasing, motion blur, light, or weather changes. Running NeuroGPR as an overall multi–neural network workload on Tianjic showcases its advantages with 10.5 times lower latency and 43.6% lower power consumption than the commonly used mobile robot processor Jetson Xavier NX.

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Section: Discussionmentioning

confidence: 99%

Brain-inspired multimodal hybrid neural network for robot place recognition

et al. 2023

Sci. Robot.

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“…A type of robot, named Brain-inspired Intelligent Robotics, merits our attention (Qiao et al , 2023). This robot emulates human brain processes for decision-making and control.…”

Section: Energy Optimization For Autonomous Mobile Robotsmentioning

confidence: 99%

A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

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Purpose This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions. Design/methodology/approach The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems. Findings The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints. Research limitations/implications The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs. Originality/value This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

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“…However, the LfD method still needs to be improved due to the limited generalization capability. The existing LfD method usually only learns the motor skills taught by human beings, but cannot well learn human perception and decision-making ability, which is also one of the core issues in the field of robotics (Qiao et al , 2022; Qiao et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

A novel human-robot skill transfer method for contact-rich manipulation task

Dong

Yang

2023

RIA

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Purpose The purpose of this paper is to enhance the robot’s ability to complete multi-step contact tasks in unknown or dynamic environments, as well as the generalization ability of the same task in different environments. Design/methodology/approach This paper proposes a framework that combines learning from demonstration (LfD), behavior tree (BT) and broad learning system (BLS). First, the original dynamic motion primitive is modified to have a better generalization ability for representing motion primitives. Then, a BT based on tasks is constructed, which will select appropriate motion primitives according to the environment state and robot ontology state, and then the BLS will generate specific parameters of the motion primitives based on the state. The weights of the BLS can also be optimized after each successful execution. Findings The authors carried out the tasks of cleaning the desktop and assembling the shaft hole on Baxter and Elite robots, respectively, and both tasks were successfully completed, which proved the effectiveness of the framework. Originality/value This paper proposes a framework that combines LfD, BT and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

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Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

Cited by 50 publications

References 127 publications

Brain-inspired multimodal hybrid neural network for robot place recognition

Brain-inspired multimodal hybrid neural network for robot place recognition

A review on energy efficiency in autonomous mobile robots

A novel human-robot skill transfer method for contact-rich manipulation task

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