摘要
智能水下机器人在众多重要领域都是必不可少的一环,如水下资源探测、海洋数据勘察、水下通信作业、水下科研数据采集等。在传统路径规划中,蚁群算法被广泛应用,但是也由于蚁群在选择路径上具有一定的随机性,蚁群数量庞大,所以算法寻优速度缓慢不容易收敛,且存在局部最优等问题。因此主要针对水下机器人的三维路径规划问题进行优化,提出了一种协同进化算法。将蚁群算法与粒子群算法协同进化与动态信息素的蚁群算法进行对比,提高了算法的效率与稳定性,使种群能更快选择出更优的路径以达到到达目标节点。
Ant colony algorithm plays an important role when intelligent underwater robots carry out path planning in areas such as ocean development,underwater operation,submarine exploration,submarine rescue and life assistance.Because the ant colony algorithm has a certain randomness,together with the large number of ant colonys,it is not only slow to find the optimal solution of the algorithm,but also has interference with problems such as local optimal solutions.In this paper,a co-evolution algorithm is proposed to optimize the three-dimensional path planning problem of the underwater vehicle.By comparing the ant colony algorithm with the particle swarm algorithm and the ant colony algorithm of dynamic pheromone,the efficiency and stability of the algorithm are improved,and the population can select a better path faster to reach the target node.
作者
李承睿
尹姝呓
毛剑琳
Li Chengrui;Yin Shuyi;Mao Jianlin(Kunming University of Science and Technology,Yunnan 650000,China)
出处
《电子测量技术》
北大核心
2021年第11期73-78,共6页
Electronic Measurement Technology
关键词
水下机器人
蚁群算法
粒子群算法
协同进化算法
underwater vehicle
ant colony algorithm
particle swarm optimization algorithm
coevolution algorith
