基于IB-LBM与人工势场方法的2D机器蛇水中动态避障算法
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摘要
为了研究多冗余度、多自由度的机器蛇水下环境适应能力,针对机器蛇水下动态环境中避障控制以及路径规划的问题,提出了基于人工势场与IB-LBM(Immersed Boundary Method-Lattice Boltzmann Method)相结合的2D机器蛇水中动态避障算法。算法首先根据实际水流情况,采用LBM方法构建带有障碍的2D水下流场模型,并结合现有的蛇形曲线运动模型,引入IB方法搭建出2D机器蛇水中流固耦合模型,然后对水下运动的障碍和机器蛇加入人工势场法引力与斥力的作用,通过改变障碍影响距离、机器蛇尾部摆动振幅、机器蛇尾部摆动频率、障碍点斥力系数、目标点引力系数、水流雷诺数、动态障碍运动速度等重要参数,研究机器蛇对水中动态障碍的避障能力和避障效率。利用MATLAB多次仿真求取机器蛇水中动态避障的最优解。仿真结果表明,当各项参数适当时,该动态避障算法能够使机器蛇有效避开水下环境中动态障碍而到达目标点,为未来实现机器蛇水下监测创造了条件,有重要的理论和实验意义。
The 2D aquatic intellectual obstacle avoidance algorithm backed up by artificial potential field principle and IB-IBM( Immersed Boundary Method-Lattice Boltzmann Method) are designed to enhance the underwater adaptation of snake-like robot which is featured with high degree of redundancy and freedom. According to the actual water flow situation,Lattice Boltzmann method is firstly used to build 2D underwater flow-field model with obstacles. Then combined with the existing serpentine curve motion model,Immersed Boundary method is introduced to build a water snake robot 2D fluid solid coupling model. After that,we introduced the action of the attraction and repulsion in artificial potential field on the underwater dynamic obstacles and snake-like robot,and studied the ability and efficiency of avoiding underwater dynamic obstacles by altering obstacle's affecting range,the snake tail's swing amplitude and frequency,the repulsive point repulsion,the target point gravitational force,the Reynolds number,dynamic obstacle motion speed and direction,and other important parameters. The optimistic evaluation of each parameter are available after several simulations. This algorithm can be used in Matlab simulation experiments and the results demonstrates that the algorithm manages to help Snake-like robot avoid the underwater dynamic obstacles and get to its destinations effectively when the parameters are optimistic,which provides the conditions for the underwater monitoring of snake-like robot in the future and has important theoretical and experimental significance.
The 2D aquatic intellectual obstacle avoidance algorithm backed up by artificial potential field principle and IB-IBM( Immersed Boundary Method-Lattice Boltzmann Method) are designed to enhance the underwater adaptation of snake-like robot which is featured with high degree of redundancy and freedom. According to the actual water flow situation,Lattice Boltzmann method is firstly used to build 2D underwater flow-field model with obstacles. Then combined with the existing serpentine curve motion model,Immersed Boundary method is introduced to build a water snake robot 2D fluid solid coupling model. After that,we introduced the action of the attraction and repulsion in artificial potential field on the underwater dynamic obstacles and snake-like robot,and studied the ability and efficiency of avoiding underwater dynamic obstacles by altering obstacle's affecting range,the snake tail's swing amplitude and frequency,the repulsive point repulsion,the target point gravitational force,the Reynolds number,dynamic obstacle motion speed and direction,and other important parameters. The optimistic evaluation of each parameter are available after several simulations. This algorithm can be used in Matlab simulation experiments and the results demonstrates that the algorithm manages to help Snake-like robot avoid the underwater dynamic obstacles and get to its destinations effectively when the parameters are optimistic,which provides the conditions for the underwater monitoring of snake-like robot in the future and has important theoretical and experimental significance.
引文
[1]卢振利,李斌.蛇形机器人蜿蜒游动性能动力学仿真分析[J].机器人,2015,(6):748-53.
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[10]Ma S,Araya H,Li L.Development of acreeping locomotion snake robot[J]. International Journal of Robotics&Automation,2002,17(4):146-153.
[11]Chen L,Liu C,Shi H,et al.New robot planning algorithm based on improved artificial potential field[C]//Third International Conference on Instrumentation,Measurement,Computer,Communication and Control.IEEE,2014:228-232.
[12]Montiel O,Sepúlveda R,Orozco-Rosas U. Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field[J]. Journal of Intelligent&Robotic Systems,2015,79(2):1-21.
[2]Cheng S,Xu Y,Tang X,et al. Lattice boltzmann study of micro branch array of crossflow filtration for fluid partitioning[C]//ICME International Conference on Complex Medical Engineering. IEEE,2013:586-589.
[3]Guo Z,Shi B,Wang N.Lattice BGK model for incompressible navier–stokes equation[J]. Journal of Computational Physics,2000,165(1):288-306.
[4]Wei Q,Xu Y Q,Tian F B,et al. IB-LBM simulation on blood cell sorting with a micro-fence structure[J]. Biomedical Materials and Engineering,2014,24(1):475-81.
[5]Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[C]//IEEE International Conference on Robotics and Automation. IEEE,2003:90-98.
[6]Xu Y Q,Tang X Y,Tian F B,et al. IB-LBM simulation of the haemocyte dynamics in a stenotic capillary[J].Computer Methods in Biomechanics&Biomedical Engineering,2014,17(9):978-985.
[7]Tian F B,Luo H,Zhu L,et al. Interaction between a flexible filament and a downstream rigid body[J].Physical Review E(Statistical,Nonlinear,and Soft Matter Physics),2010,82(02).
[8]Hirose S.Biologically inspired robots:snake-like locomotors and manipulators[J]. Applied Mechanics Reviews,1995,48(03).
[9]Hirose S,Yamada H.Snake-like robots:Machine design of biologically inspired robots[J]. IEEE Robotics&Automation Magazine,2009,16(1):88-98.
[10]Ma S,Araya H,Li L.Development of acreeping locomotion snake robot[J]. International Journal of Robotics&Automation,2002,17(4):146-153.
[11]Chen L,Liu C,Shi H,et al.New robot planning algorithm based on improved artificial potential field[C]//Third International Conference on Instrumentation,Measurement,Computer,Communication and Control.IEEE,2014:228-232.
[12]Montiel O,Sepúlveda R,Orozco-Rosas U. Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field[J]. Journal of Intelligent&Robotic Systems,2015,79(2):1-21.