基于猫群算法的最优路径避障算法  

Prediction and Obstacle Avoidance Detection Algorithm Based on Driverless

作　　者：霍娜[1] 王溢琴[1] HUO Na;WANG Yiqin(Department of Information technology and Engineering,Jinzhong University,Jinzhong 030619)

机构地区：[1]晋中学院信息技术与工程系,晋中030619

出　　处：《计算机与数字工程》2024年第2期411-415,共5页Computer & Digital Engineering

基　　金：山西省教育科学“十三五”规划2020年度“互联网+教育”专项课题“学习分析视域下大学生在线学情诊断与预警研究”(编号:HLW-20111);晋中学院“1331工程”创客团队建设计划项目“混合教学大数据分析”实验室(编号:jzxycktd2019039)资助。

摘　　要：针对移动无人驾驶汽车系统中在杂乱环境中到达未标记目标位置的最佳轨迹问题,提出了基于猫群算法的最优路径避障算法。首先,推导出适合同时查找轨迹和目标位置的系统误差模型,将原有控制问题转化为不确定参数的自动更新率和路径最大距离最小化的问题。其次,通过在线估计自动更新率并保证为最小长度和无碰撞情况下,分配策略使行驶的最大距离最小化。最后,通过实验仿真表明算法能够准确地用于路径预测与避障检测,抑制系统参数不确定性对控制系统的影响。This paper proposes the optimal path avoidance algorithm based on the cat-cluster algorithm for the optimal trajectory of unmarked target position in a chaotic environment.First,a systematic error model for the controller design that is suitable for the simultaneous search of the trajectory and the target position will be derived,and the original control problem is transformed into the problem of the automatic update rate of the uncertain parameters and the minimization of the maximum path distance.Secondly,by estimating the automatic update rate online,the trajectory generated by the proposed algorithm is guaranteed to be the minimum length and collision-free,and the allocation strategy minimizes the maximum travel distance.Finally,experimental simulations show that the proposed algorithm can be used accurately for path prediction and obstacle avoidance detection,and suppress the influence of system parameter uncertainty on the control system.

关 键 词：避障 最优路径规划 连通性 轨迹跟踪 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]