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Special Issue "Sensors, Motor Coordination, and High-Level Cognition in Bio-Inspired Robotics"

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

Deadline for manuscript submissions: 30 October 2021.

Special Issue Editors

Dr. Julien Serres
E-Mail Website
Guest Editor
Aix Marseille University, The Institute of Movement Sciences UMR7287, 13288 Marseille Cedex 09, France
Interests: biorobotics; bio-inspired robotics; optic flow; visual guidance; celestial compass; bio-inspired navigation
Dr. Poramate Manoonpong
E-Mail Website
Guest Editor
Embodied AI & Neurorobotics Lab, SDU Biorobotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
Interests: embodied neurorobotics; bio-inspired robotics; neural locomotion control; learning and plasticity; machine learning for robotics
Dr. Paolo Arena
E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettric Elettronica e Informatica, University of Catania, Viale A. Doria, 6, 95125 Catania, Italy
Interests: nonlinear systems modeling and control; bio-inspired robots, adaptive locomotion; learning systems; insect brain architectures
Prof. Dr. Luca Patanè
E-Mail Website
Guest Editor
Department of Engineering, University of Messina, Contrada di Dio, S. Agata, 98166 Messina, Italy
Interests: Nonlinear Systems Modeling and Control; Bio-Robotics; Locomotion control, Spiking Neural Networks, Insect-Inspired Control Systems; System Identification and Soft Sensor Development
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Biomimetics is the development of innovative technologies through the distillation of principles from Nature. Bio-inspired robots are formed by combining at least one biological principle embodied either in their perceptive systems or in their locomotor systems, or both at once. Many animals show remarkable locomotion, navigation, and even high-level cognitive skills to deal with difficult or dynamically changing environmental conditions by efficiently extracting information from their surrounding environment in an attempt to reach their goal. This is sometimes attained through the efficient exploitation of a relatively simple and distributed brain architecture embodied into an extremely resilient and incredibly sensorized body structure. Understanding and mimicking such reliable sensors, motor coordination, and high-level control systems is thus necessary for new generations of robots operating outdoors to reach a similar level of performance to animals. 

This Special Issue will focus on all aspects related to bio-inspired robotic architectures and their constituents, including sensors, motor coordination, and high-level cognitive functions.

Dr. Julien Serres
Dr. Poramate Manoonpong
Dr. Paolo Arena
Dr. Luca Patanè
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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

  • Bio-inspired actuators
  • Bio-inspired sensors
  • Bio-inspired navigation
  • Learning in bio-inspired robots
  • Sensory-motor coordination
  • Soft robotics
  • Legged robotics
  • Unconventional perception
  • Polarized vision
  • Neural control
  • Spiking neural networks
  • Bio-inspired brain models
  • Neuromorphic systems

Published Papers (1 paper)

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Research

Article
Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms
Sensors 2021, 21(9), 3240; https://doi.org/10.3390/s21093240 - 07 May 2021
Viewed by 472
Abstract
In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training [...] Read more.
In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious task. Until recently, various ANN to SNN conversion methods in the literature have been proposed to train deep SNN models. Nevertheless, these methods require hundreds to thousands of time-steps for training and still cannot attain good SNN performance. This work proposes a customized model (VGG, ResNet) architecture to train deep convolutional spiking neural networks. In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks. Moreover, this work also proposes fewer time-steps for training SNNs with surrogate gradient descent. During the training with surrogate gradient descent backpropagation, overfitting problems have been encountered. To overcome these problems, this work refines the SNN based dropout technique with surrogate gradient descent. The proposed customized SNN models achieve good classification results on both private and public datasets. In this work, several experiments have been carried out on an embedded platform (NVIDIA JETSON TX2 board), where the deployment of customized SNN models has been extensively conducted. Performance validations have been carried out in terms of processing time and inference accuracy between PC and embedded platforms, showing that the proposed customized models and training techniques are feasible for achieving a better performance on various datasets such as CIFAR-10, MNIST, SVHN, and private KITTI and Korean License plate dataset. Full article
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