Abstract
We have entered a new era, “Industry 4.0”, that sees the overall industry marching toward an epoch of man–machine symbiosis and intelligent production. The developers of so-called “intelligent” systems must attempt to seriously take into account all possible situations that might occur in the real world, to minimize unexpected errors. By contrast, biological systems possess comparatively better “adaptability” than man-made machines, as they possess a self-organizing learning that plays an indispensable role. The objective of this study was to apply a malleable learning system to the movement control of a snake-like robot, to investigate issues related to self-organizing dynamics. An artificial neuromolecular (ANM) system previously developed in our laboratory was used to control the movements of an eight-joint snake-like robot (called Snaky). The neuromolecular model is a multilevel neural network that abstracts biological structure–function relationships into the system’s structure, in particular into its intraneuronal structure. With this feature, the system possesses structure richness in generating a broad range of dynamics that allows it to learn how to complete the assigned tasks in a self-organizing manner. The activation and rotation angle of each motor are dependent on the firing activity of neurons that control the motor. An evolutionary learning algorithm is used to train the system to complete the assigned tasks. The key issues addressed include the self-organizing learning capability of the ANM system in a physical environment. The experimental results show that Snaky was capable of learning in a continuous manner. We also examined how the ANM system controlled the angle of each of Snaky’s joints, to complete each assigned task. The result might provide us with another dimension of information on how to design the movement of a snake-like robot.
Highlights
The advancement of artificial intelligence, especially deep learning, coupled with the recent development of the Internet of Things, has once again received great attention by the public
We have shown that the close “structure/function” relation facilitates the artificial neuromolecular (ANM) system in generating sufficient dynamics for shaping neurons into special input/output pattern transducers that meet the needs of a specific task [4,5]
The information processing (IP) neurons in this study are motivated by the dynamics that reflect molecular processes believed to be operative in real neurons, in particular processes connected with second messenger signals and cytoskeleton–membrane interactions
Summary
The advancement of artificial intelligence, especially deep learning, coupled with the recent development of the Internet of Things, has once again received great attention by the public. In response to the need to correct this problem, most traditional artificial intelligence approaches must carefully consider and evaluate all possible situations in real world operations, to ensure that everything is under control Without doubt, in such conditions, a carefully designed system should perform as expected, as all the problems related to the operational environment. The steps involved are to first study the snake’s motion curve and to apply mechanical operation principles to create snake-type motion [8] These steps include deriving the velocities of different snake segments to perform rectilinear motion [9,10,11], using the position of the motor and leaning against the rotation of the wheel to cause the snake-shaped robot to move forward [12], and using Watt-I planar linkage mechanism to control a biped water-running robot to generate propulsion force [13].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
