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

2020 ◽  
Vol 42 (10) ◽  
pp. 1840-1857
Author(s):  
Dongfang Li ◽  
Zhenhua Pan ◽  
Hongbin Deng
Keyword(s):  
Flow Field ◽  
Obstacle Avoidance ◽  
Lattice Boltzmann ◽  
Potential Field ◽  
Control Algorithm ◽  
Immersed Boundary ◽  
Artificial Potential Field ◽  
Two Dimensional ◽  
Avoidance Control ◽  
Boltzmann Method

In order to study the adaptability of a multi-redundancy and multi-degree-of-freedom snake-like robot to underwater motion, a two-dimensional (2-D) obstacle avoidance control algorithm for a snake-like robot based on immersed boundary-lattice Boltzmann method (IB-LBM) and improved artificial potential field (APF) is proposed in this paper. Firstly, the non-linear flow field model is established under the framework of LBM, and the IB method is introduced to establish a fluid solid coupling of a 2-D soft snake-like robot. Then, the obstacle avoidance of a snake-like robot in a flow field is realized by optimizing the curvature equation of the serpentine curve and eliminating the local minimum in APF method. Finally, the effects by exerted different control parameters on a snake-like robot’s obstacle avoidance capability are analyzed via MATLAB simulation experiment, by which we can find the optimal parameter of the obstacle avoidance and testify the validity of the proposed control algorithm.

scholarly journals UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

2021 ◽  
Author(s):  
Ning Wang ◽  
Jiyang Dai ◽  
Jin Ying
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Control Algorithm ◽  
Influence Factor ◽  
Order System ◽  
Attractive Potential ◽  
Artificial Potential Field ◽  
Particle Model ◽  
Field Function ◽  
Avoidance Control

AbstractAiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

EFFECT OF MEMBRANE BENDING STIFFNESS ON THE DEFORMATION OF ELASTIC CAPSULES IN EXTENSIONAL FLOW: A LATTICE BOLTZMANN STUDY

2007 ◽  
Vol 18 (08) ◽  
pp. 1277-1291 ◽  
Author(s):  
Y. SUI ◽  
Y. T. CHEW ◽  
P. ROY ◽  
H. T. LOW
Keyword(s):  
Lattice Boltzmann ◽  
Present Model ◽  
Extensional Flow ◽  
Bending Stiffness ◽  
Immersed Boundary ◽  
Two Dimensional ◽  
Bending Rigidity ◽  
Elastic Membranes ◽  
Membrane Bending ◽  
Boltzmann Method

The transient deformation of liquid capsules enclosed by elastic membranes in two-dimensional extensional flow is studied numerically, using an improved immersed boundary-lattice Boltzmann method. The purpose of the present study is to investigate the effect of interfacial bending stiffness on the deformation of such capsules, under the subcritical elasticity capillary number conditions. The present model can simulate flow-induced deformation of capsules with arbitrary resting shapes (concerning the in-plane tension) and bending-free configurations. The deformation of capsules with initially circular, elliptical, and biconcave resting shapes was investigated in the present study; the capsules' bending-free configurations were considered as either circular shapes or their initially resting shapes. The results show that for capsules with bending-free configuration as circles, membrane bending rigidity has significant rounding effect on the steady deformed profiles. For elliptical and biconcave capsules with resting shapes as the bending-free configurations, it is found that with the bending stiffness increasing, the capsules' steady shapes are more akin to their initial shapes.

2215 Topology optimization of two-dimensional flow field shape by means of lattice Boltzmann method

2014 ◽  
Vol 2014.11 (0) ◽  
pp. _2215-1_-_2215-6_
Author(s):  
Hiroshi TAKEDA ◽  
Toshiro MATSUMOTO ◽  
Masato YOSHINO ◽  
Takayuki YAMADA ◽  
Toru TAKAHASHI ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Flow Field ◽  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Two Dimensional ◽  
Dimensional Flow ◽  
Two Dimensional Flow ◽  
Boltzmann Method

Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm

Robotica ◽  
2019 ◽  
Vol 37 (11) ◽  
pp. 1883-1903 ◽  
Author(s):  
Zhenhua Pan ◽  
Dongfang Li ◽  
Kun Yang ◽  
Hongbin Deng
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Tracking Control ◽  
Formation Control ◽  
Control Algorithm ◽  
Situational Awareness ◽  
Artificial Potential Field ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Multi Robot

SummaryAs for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

scholarly journals UAV formation obstacle avoidance control method based on artificial potential field and consistency

2021 ◽  
Vol 2083 (4) ◽  
pp. 042029
Author(s):  
Boyu Wei
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Control Method ◽  
Artificial Potential Field ◽  
Time Varying ◽  
Complex Environments ◽  
Control Scheme ◽  
Avoidance Control ◽  
Multi Agent

Abstract As a typical multi-agent formation, UAV formation is playing an increasingly powerful role in the civilian and military fields. Obstacle avoidance, as an important technology in controlling formation, determines the application prospects of UAVs. This paper studies the time-varying formation of UAVs with interactive topology to avoid obstacles, aiming to improve the ability of UAV formations to deal with complex environments while traveling. Firstly, a repulsive force field is reasonably introduced based on the existing control scheme, and an improved distributed time-varying formation control scheme based on artificial potential field is proposed. Then combined with the basic idea of model predictive control, an obstacle avoidance strategy in which UAV obstacle avoidance and formation shaping are carried out simultaneously is proposed. Finally, a time-varying formation simulation experiment containing four UAVs was carried out to verify the validity of the results.

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