Effectiveness of Bio Inspired Robotics in Studying Biological Systems, and Looking for the Mechanisms that May Solve a Problem in the Engineering Field

Autor: Dr. SK Althaf Hussain Basha, N Dinesh Kumar
Rok vydání: 2022
Zdroj: Technoarete Transactions on Industrial Robotics and Automation Systems. 2
ISSN: 2583-1941
Popis: Bio-inspired robots have been developed for creating natural things insight robots. These kinds of robots have the capability to interact with human easily. Nowadays, many scientists are trying to make an advanced bio-inspired robot that also makes able to have an impact on society or helps individuals. These kinds of development generally use for logistics, manufacturing, surgery, supply, and car driving. Bio-inspired robot use high-capacity sensors, high-resolution cameras, chips, and other major kinds of essential technologies. This study has also focused on the importance of bioinspired robots that plays a significant role in assembling, carrying, sawing, and other essential curriculum. Interaction between robots and human increase the advantages and strength of the activities and it can provide the desired benefits to individuals. Accordingly, this paper also tries to find the impact of the biological system and its significance in the development of bio-inspired robots. Interaction between human and robots make a huge and effective impact on society and people get inspired to adopt technological facilities in their daily life. In a biological system, bio-robotics also help to develop the growth of technological transformation. Biological studies get inspiration to create designs of advanced and intelligent robotics for conducting diverse human-like activities such as swimming, terrestrial movements, and flying. That also helps to understand the evolution of biological organisms within an advanced dynamic environment. Keywords : Bio-inspired robots, bio-inspired robotic platforms, Robotics Traction Unit (RTU), AI Farming, AI Technology, Magnet-polymer, hybrid continuum cable-driven robot (HCDR), Deep learning models, acrylonitrile-butadiene-styrene, PDMS, ABC, ACO, PSO.
Databáze: OpenAIRE