基于改进细菌觅食优化的无人艇自主避碰算法

针对无人艇的避碰规划问题，设计一种基于改进细菌觅食优化（BFO）的自主避碰算法. 针对基本BFO算法收敛速度慢、寻优精度低、稳定性低的不足，设计自适应递减分维趋化步长代替固定步长以实现步长的自适应调整，提出优适探寻游动方法解决基本BFO算法无效游动与重复游动的缺陷，设计自适应迁徙概率代替固定迁徙概率以解决基本BFO算法可能导致精英个体丢失的情况. 函数测试仿真表明，改进BFO算法具有更好的收敛速度、寻优精度以及稳定性.改进算法应用于无人艇避碰仿真结果证明，该改进算法能够快速安全地实现无人艇在动态障碍下的自主避碰.
To solve the collision avoidance planning problem of unmanned surface vehicle（USV）, an automatic collision avoidance algorithm was designed based on improved bacterial foraging optimization（BFO）. Aiming at the shortcomings of slow convergence speed, low optimization precision and low stability resulted from basic BFO, an adaptive diminishing fractal dimension chemotactic step length was designed to replace the fixed step length in order to realize the adaptive adjustment of step length. The optimal search method was put forward to solve the defects of ineffective swimming and repeated swimming in the basic BFO algorithm. An adaptive migration probability was designed instead of fixed migration probability to solve the problem of elite individual loss caused by basic BFO algorithm. The function test simulation shows that the improved BFO algorithm has better convergence speed, better optimization precision and better stability. The improved algorithm was applied into USV collision avoidance simulation, and results show that the improved algorithm can quickly and safely realize the autonomous collision avoidance of UAV under dynamic obstacles.