Trajectory tracking control law of multi-joint snake-like robot based on improved snake-like curve in flow field

Abstract

Aiming at the problem of trajectory tracking between joints of the multi-joint snake-like robot in the flow fields, a trajectory tracking control law proposed based on the improved snake-like curve of a multi-joint snake-like robot to avoid obstacles in the flow fields is studied. Firstly, considering the external disturbance that the fluid environment may impose on the multijoint snake-like robot system, from the point of view of probability, the fluid-solid coupling models of the obstacle channel and multi-joint snake-like robot are established in the flow field by using immersed boundary-lattice Boltzmann method algorithm, which solves the problem of nonlinear fluid motion that cannot be explained by solving the Navier-Stokes (N-S) equation. Then, a potential function is applied to the multi-joint snake-like robot so that the head of the robot can avoid obstacles in the fluid smoothly. By improving the snake-like motion equation, the snake-like curve trajectory tracking function of each joint of the multi-joint snake-like robot with time variation is obtained, which enables the tail joints of the snake-like robot to track the motion trajectory of the head joints. Finally, the effects of different flow field density, velocity, and Reynolds numbers on trajectory tracking of the multi-joint snake-like robot are studied by MATLAB simulations and experiments. The theoretical analysis and numerical simulation show that the designed trajectory tracking control law can make the multi-joint snake-like robot track the trajectory of the front joint when the robot encounters obstacles and make the robot stabilize the lateral distance, longitudinal distance, and direction angle, so as to effectively avoid obstacles. The simulation and experimental results verify the effectiveness of the trajectory tracking control law.

An Adaptive Trajectory Tracking Controller of a Multi-joint Snake Robot Considering Non-holonomic Constraints

“…Finally, a suitable Lyapunov function is found to verify the asymptotic stability of the controller. This method improves the deficiency of the reference [13,14], which does not verify the stability of the robot trajectory tracking theoretically. To verify the effectiveness of an adaptive trajectory tracking controller of a multi-joint snake robot, experiments are carried out.…”

“…It is proved that the Serpentine curve has a higher motion effect than the Serpenoid curve from the perspective of motion efficiency, which makes a contribution to the three-dimensional kinematics modelling of the robots. Reference [13,14] has solved the problem of the fluid-structure coupling obstacle avoidance of a snake robot with the IB-LBM method. This method can realize the trajectory tracking of the robot in underwater obstacle avoidance, but the stability of the trajectory tracking of the robot is not verified theoretically.…”

Motion Planning Algorithm of a Multi-Joint Snake-Like Robot Based on Improved Serpenoid Curve

“…In this section, the locomotion pattern of biological snakes on land and its motion state in the winding motion are observed as shown in Fig.4 [35]. The Serpenoid curve equation (26) proposed by Professor Hirose in Japan is referred [7], as shown in Fig.5.…”

“…In this paper, a motion planning algorithm of a wheeled multi-joint snake-like robot with non-holomorphic constraint based on improved Serpenoid curve equation is proposed to realize the winding and sideslip motion of the robot in plane. This method is based on the two-dimensional motion plane of a multi-joint snake-like robot [9], [26]. A robot with non-holonomic constraint allows dimensions of its actuator to be less than the number of generalized coordinates in the system.…”

Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method

“…In the macroscopic system of underwater fluid, the micro-motion of each molecule follows the law of mechanics. In order to save computation time, reduce computational complexity, and to solve the non-linear fluid problem that is hard to be explained by Navier-Stokes equation, the LBM is introduced to simplify the solution to non-linear fluid and realize parallel computation from the perspective of probability (Li et al, 2019; Yuanqing et al, 2014). The LBM method only needs to calculate the probability of each molecule in certain state, and then the macroscopic parameters of the system can be calculated.…”