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Robotic Control Based on Neuromorphic Approaches and Hardware
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Robotic Control Based on Neuromorphic Approaches and Hardware

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 12809

Special Issue Editors

Prof. Dr. Alois C. Knoll

E-Mail Website
Guest Editor
Institut für Informatik VI, Technische Universität München, Boltzmannstraße 3, 85748, Garching bei München, Germany
Interests: brain-inspired intelligence; cognitive; medical and sensor-based robotics; embedded systems
Dr. Zhenshan Bing

E-Mail Website
Guest Editor
Institut für Informatik VI, Technische Universität München, Boltzmannstraße 3, 85748, Garching bei München, Germany
Interests: brain-inspired intelligence; biological robotics

Special Issue Information

Dear Colleagues,

Emerging neuromorphic engineering technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots with neuromorphic approaches, such as the spiking neural networks (SNNs) and neuromorphic hardware (e.g., SpiNNaker, Loihi, and dynamic vision sensors). With the potential to offer fundamental improvements in computational capabilities such as data processing speed and lower power consumption, neuromorphic approaches have become increasingly important for robot applications, especially in mobile applications where real-time responses are vital and energy supply is limited.

This Special Issue on Robotic Control Based on Spiking Neural Networks and Neuromorphic Hardware invites researchers to present state-of-the-art theories, models, control strategies, and applications with advanced neuromorphic computing hardware and sensors, which are closely related to robotics. More specifically, we are looking for methods and applications for integrating SNN-based controllers into neuromorphic devices and studies on sensing the environment with neuromorphic sensors.

The topics relevant to this Special Issue include, but are not limited to, the following:

  • Neuromorphic approaches for robotics;
  • Design and applications of neuromorphic hardware and sensors;
  • Neuromorphic vision, auditory, and olfactory sensing;
  • Bioinspired robots;
  • Theory and modelling of SNN;
  • SNN for brain-inspired artificial intelligence;
  • SNN for robotics.

Prof. Dr. Alois C. Knoll
Dr. Zhenshan Bing
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neuromorphic approaches for robotics
  • neuromorphic processors design and applications
  • neuromorphic vision, auditory, and olfactory sensing

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Published Papers (4 papers)

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Research

16 pages, 5670 KiB  
Article
Adaptive SNN for Anthropomorphic Finger Control
by Mircea Hulea, George Iulian Uleru and Constantin Florin Caruntu
Sensors 2021, 21(8), 2730; https://doi.org/10.3390/s21082730 - 13 Apr 2021
Cited by 7 | Viewed by 3088
Abstract
Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents [...] Read more.
Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents a simple structure of an adaptive spiking neural network implemented in analogue hardware that can be trained using Hebbian learning mechanisms to rotate the metacarpophalangeal joint of a robotic finger towards targeted angle intervals. Being bioinspired, the spiking neural network drives actuators made of shape memory alloy and receives feedback from neuromorphic sensors that convert the joint rotation angle and compression force into the spiking frequency. The adaptive SNN activates independent neural paths that correspond to angle intervals and learns in which of these intervals the rotation the finger rotation is stopped by an external force. Learning occurs when angle-specific neural paths are stimulated concurrently with the supraliminar stimulus that activates all the neurons that inhibit the SNN output stopping the finger. The results showed that after learning, the finger stopped in the angle interval in which the angle-specific neural path was active, without the activation of the supraliminar stimulus. The proposed concept can be used to implement control units for anthropomorphic robots that are able to learn motions unsupervised, based on principles of high biological plausibility. Full article
(This article belongs to the Special Issue Robotic Control Based on Neuromorphic Approaches and Hardware)
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15 pages, 10830 KiB  
Article
Spatial Memory in a Spiking Neural Network with Robot Embodiment
by Sergey A. Lobov, Alexey I. Zharinov, Valeri A. Makarov and Victor B. Kazantsev
Sensors 2021, 21(8), 2678; https://doi.org/10.3390/s21082678 - 10 Apr 2021
Cited by 19 | Viewed by 3601
Abstract
Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then [...] Read more.
Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world. Full article
(This article belongs to the Special Issue Robotic Control Based on Neuromorphic Approaches and Hardware)
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17 pages, 4487 KiB  
Article
Spatial Topological Relation Analysis for Cluttered Scenes
by Yu Fu, Mantian Li, Xinyi Zhang, Sen Zhang, Chunyu Wei, Wei Guo, Hegao Cai, Lining Sun, Pengfei Wang and Fusheng Zha
Sensors 2020, 20(24), 7181; https://doi.org/10.3390/s20247181 - 15 Dec 2020
Cited by 3 | Viewed by 2078
Abstract
The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this [...] Read more.
The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this paper, we presented the classification of spatial topological relations by dividing the intersection space into six parts. In order to improve accuracy and reduce computing time, convex hulls are utilized to represent the boundary of objects and the spatial topological relations can be determined by the category of points in point clouds. We verified our method on the datasets. The result demonstrated that we have great improvement comparing with the previous method. Full article
(This article belongs to the Special Issue Robotic Control Based on Neuromorphic Approaches and Hardware)
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31 pages, 9184 KiB  
Article
Attitude Trajectory Optimization to Ensure Balance Hexapod Locomotion
by Chen Chen, Wei Guo, Pengfei Wang, Lining Sun, Fusheng Zha, Junyi Shi and Mantian Li
Sensors 2020, 20(21), 6295; https://doi.org/10.3390/s20216295 - 5 Nov 2020
Cited by 5 | Viewed by 2979
Abstract
This paper proposes a simple attitude trajectory optimization method to enhance the walking balance of a large-size hexapod robot. To achieve balance motion control of a large-size hexapod robot on different outdoor terrains, we planned the balance attitude trajectories of the robot during [...] Read more.
This paper proposes a simple attitude trajectory optimization method to enhance the walking balance of a large-size hexapod robot. To achieve balance motion control of a large-size hexapod robot on different outdoor terrains, we planned the balance attitude trajectories of the robot during walking and introduced how leg trajectories are generated based on the planned attitude trajectories. While planning the attitude trajectories, high order polynomial interpolation was employed with attitude fluctuation counteraction considered. Constraints that the planned attitude trajectories must satisfy during walking were well-considered. The trajectory of the swing leg was well designed with the terrain attitude considered to improve the environmental adaptability of the robot during the attitude adjustment process, and the trajectory of the support leg was automatically generated to satisfy the demand of the balance attitude trajectories planned. Comparative experiments of the real large-size hexapod robot walking on different terrains were carried out to validate the effectiveness and applicability of the attitude trajectory optimization method proposed, which demonstrated that, compared with the currently developed balance motion controllers, the attitude trajectory optimization method proposed can simplify the control system design and improve the walking balance of a hexapod robot. Full article
(This article belongs to the Special Issue Robotic Control Based on Neuromorphic Approaches and Hardware)
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