4.7 Article

Collaborative obstacle avoidance algorithm of multiple bionic snake robots in fluid based on IB-LBM

Journal

ISA TRANSACTIONS
Volume 122, Issue -, Pages 271-280

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2021.04.048

Keywords

Multiple bionic snake robots; IB-LBM; Collaborative obstacle avoidance

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This paper presents a collaborative obstacle avoidance algorithm for multiple bionic snake robots in fluid based on IB-LBM. The algorithm allows multiple robots to avoid different obstacles in the fluid, with high parallelism and the ability to simulate complex nonlinear phenomena.
This paper presents a collaborative obstacle avoidance algorithm of multiple bionic snake robots in fluid based on IB-LBM. The method can make the multiple bionic snake robots avoid different obstacles in the fluid under the control of the improved Serpenoid curve function. The proposed method has high parallelism, can simulate the complex non-linear phenomenon of the multiple snake robots, deal with the complex boundary conditions of the robot, and reduce the conversion of the computational grid. Firstly, a non-linear fluid model is established by LBM, which solves the non-linear problem that the classical Navier-Stokes equations cannot explain the random motion. Secondly, the force source boundary model of multiple bionic snake robots is established by IBM, which saves the calculation time, improves the calculation efficiency and system stability. After that, each bionic snake robot is given a special force to make the robots collaborate with each other and non-colliding with each other in the process of the obstacle avoidance. Finally, through simulation experiments, the trajectory of multiple bionic snake robots avoiding different number of the obstacles in the fluid is analyzed and the collaborative obstacle avoidance process of multiple bionic snake robots in fluid is observed. The validity of the collaborative obstacle avoidance algorithm of multiple bionic snake robots in fluid based on the IB-LBM is verified. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

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