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Designs in Bionic Robots: Compared with Conventional Robots
- 1 Chongqing Depu Foreign Language School, Chongqing, China
* Author to whom correspondence should be addressed.
Abstract
With the advancement in relative technology, robotics is gaining an increasing amount of attention among engineers and researchers as it is a current focus on enabling people’s lives to become more convenient. However, there is another type of robot designed very differently from functional robots like delivery robots and quadrotors that most people are familiar with, bionic robots. This is a kind of robots that gets design inspiration from real animals in nature, from the design of structure to altitudes and mechanisms of motion. How bionic robots are designed, and how the conceptional difference of bionic robots with other robots is reflected on their physical designs are the focuses of this paper. In this paper, four basic types of bionic robots are introduced and 17 existing designs of bio-inspired vehicles are included. 10 bionic aerial vehicles in 3 smaller classifications are compared to 3 conventional aerial vehicles in order to figure out differences in 3 aspects (structure design, sensors, and control method). The results show that bionic robots usually have more degrees of freedom in order to mimic animals’ attitudes, and are lighter in weight. Typically, bio-inspired aerial vehicles employ different wing systems compared with conventional ones. Additionally, while conventional vehicles favour PID more because of its ease of construction, bionic robots use machine learning techniques. Both types of robots use the same sensors, which is necessary for them to detect their environment. However, their control methods differ due to different purposes.
Keywords
Bionic robots, conventional robots, designs, control methods, aerial vehiclesBionic robots, conventional robots, designs, control methods, aerial vehicles
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Cite this article
Zhang,M. (2024). Designs in Bionic Robots: Compared with Conventional Robots. Applied and Computational Engineering,81,8-15.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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