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Bioinspired Robotics

This special issue belongs to the section “Sensors and Robotics“.

Special Issue Information

Dear colleagues,

Inspired by nature and biology, bioinspired robotics focuses on the novel design and control of robots. These robots include humanoid robots, crawling robots, jumping robots, modular robots, legged robots, flying robots, swimming robots, cobots, and climbing robots. Most of these robots have some locomotion system, and they often learn from nature. To contribute to this area extensive research has been conducted on biosensors, actuators, artificial muscles, biomaterials, bio-inspired control, artificial intelligence, adaptive learning, control using biological signals such as EMG and EEG, anthropomorphic design of robots, motion planning, vision-based control, autonomous navigation/control, and intelligent control. This Special Issue aims to gather cutting-edge research contributions in bioinspired robotics, focusing on the novel design and control of bioinspired robots in different application settings/environments, including home/clinical use, industry, search and rescue,  underwater, space, and military applications.

Dr. Mohammad H. Rahman
Dr. Jawhar Ghommam
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

  • bio-inspired design
  • swarm robots, artificial muscles
  • adaptive control of bio-inspired robots
  • biosensors
  • soft robots
  • swarming
  • co-robotics
  • biomaterials
  • biolocomotion
  • energy harvesting
  • autonomy
  • humanoid robots
  • and modular robots
  • wearable robots
  • crawling robots, jumping robots, modular robots, legged robots, flying robots, swimming robots, climbing robot, nonlinear control, and adaptive learning

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Sensors - ISSN 1424-8220