基于人工势场与IB-LBM的机器蛇水中2D避障控制算法

The 2D Aquatic Obstacle Avoidance Control Algorithm of the Snake-LikeRobot Based on Artificial Potential Field and IB-LBM

  • 摘要: 为了提高多冗余度、多自由度机器蛇水下环境运动适应能力,提出了基于人工势场与IB-LBM (immersed boundary method-lattice Boltzmann method)相结合的机器蛇水中2D智能避障算法.首先,采用格子Boltzmann方法描述2D水中障碍模型、构造统一形式.然后,运用浸入边界法,结合现有的蛇形曲线运动方程,在计入人工势场法引力和斥力作用的情况下,推导得到机器蛇2D水中避障模型.之后,通过改变障碍影响距离、机器蛇摆动振幅、摆动频率、障碍点斥力增益系数、雷诺数以及目标点引力增益系数等重要参数,研究机器蛇在不同情况下的避障效率和避障安全性.最后,通过多次仿真求取各项参数的最优值.仿真结果表明,在各项参数都最优时,该算法能使机器蛇快速、安全、有效地避开水下复杂环境中的静态障碍而到达目标点.该方法不仅能够充分研究机器蛇在水中的流固耦合特性,获得实时避障效果,而且能够利用已知的环境信息生成最优路径.

     

    Abstract: To improve the underwater adaptability of the multi-DOF (degree of freedom) snake-like robot with high redundancy,a 2D aquatic intelligent obstacle avoidance algorithm based on artificial potential field and IB-IBM (immersed boundary method-lattice Boltzmann method) is proposed.Firstly,the lattice Boltzmann method is used to describe 2D aquatic obstacle model and construct the unified form.Then,by applying immersed boundary method and combining the existing snake curve motion equation,the 2D aquatic obstacle avoidance model is deduced under the attraction and repulsion action of artificial potential field.Afterwards,the obstacle avoidance efficiency and safety of the snake-like robot are studied under different conditions,including changing obstacle distances,swing amplitude and swing frequency of the snake-like robot, the repulsive gains of obstacle points,the Reynolds number,the attractive gains of target points as well as other important parameters.Finally,the optimal values of every parameter are obtained by several simulations.The simulation results prove that the algorithm enables the snake-like robot to avoid the static obstacles in complex underwater environment and reach its destination swiftly,safely and efficiently when the parameters are optimal.The method can not only fully study the fluid structure coupling characteristics of the underwater snake-like robot and achieve the real-time obstacle avoidance effect,but also generate the optimal path by using the known environmental information.

     

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