Research on path planning of underwater inspection robot based on improved ant colony algorithm
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摘要: 针对传统蚁群算法在水下巡检机器人路径规划时存在收敛速度慢、易陷入局部最优、水下环境适应性差的问题, 提出一种改进蚁群算法。首先优化蚁群信息素更新策略, 构建包含路径长度、安全性、平滑性与水流适配性的综合评价函数, 通过路径适应度高低来动态调整信息素释放系数大小, 并根据迭代进度动态调整信息素挥发系数, 在算法迭代后期加快收敛速度; 其次在启发函数中引入水流因素与障碍物距离因素, 引导蚂蚁选择质量更好的路径; 随后融合模拟退火机制避免算法陷入局部最优; 最后结合B样条曲线对路径做平滑性处理, 减少水下机器人的能耗。仿真结果表明, 改进蚁群算法不仅收敛速度更快、转点数量更少, 还缩短了最优路径长度。Abstract: Aiming at the problems of slow convergence speed, easy to fall into local optimum and poor environmental adaptability of traditional ant colony algorithm in path planning of underwater inspection robot, an improved ant colony algorithm suitable for path planning of underwater inspection robot is proposed. Firstly, the ant colony pheromone update strategy is optimized, and a comprehensive evaluation function including path length, safety, smoothness and water flow adaptability is constructed. The pheromone release coefficient is dynamically adjusted by the path fitness. Adjust the pheromone volatilization coefficient to adjust with the iterative process, and speed up the convergence speed in the later stage of the algorithm iteration; the water flow factor and obstacle distance factor are introduced into the heuristic function to guide the ants to choose a better path. Then the simulated annealing mechanism is integrated to avoid the algorithm falling into local optimum. Finally, the B-spline curve is used to smooth the path to reduce the energy consumption of the underwater vehicle. The simulation results show that the improved ant colony algorithm not only has faster convergence speed and fewer turning points, but also significantly shortens the optimal path length.
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图 3 20×20栅格下传统蚁群算法路径规划结果
Figure 3. Path planning results of traditional ant colony algorithm in 20×20 grid
图 5 20×20栅格下改进蚁群算法路径规划结果
Figure 5. Path planning results of improved ant colony algorithm in 20×20 grid
图 6 30×30栅格下传统蚁群算法路径规划结果
Figure 6. Path planning results of traditional ant colony algorithm in 30×30 grid
图 8 30×30栅格下改进蚁群算法路径规划结果
Figure 8. Path planning results of improved ant colony algorithm in 30×30 grid
表 1 传统蚁群算法仿真参数
Table 1. Simulation parameters of traditional ant colony algorithm
蚁群
数量最大迭
代次数信息素重
要性因子启发函数
重要性因子信息素挥
发系数30 200 1.0 5.0 0.1 
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表 2 改进蚁群算法仿真参数
Table 2. Simulation parameters of improved ant colony algorithm
蚁群
数量最大迭
代次数退火初
始温度路径长
度权重安全性
权重平滑性
权重水流
权重最小安
全距离30 200 15° 0.7 0.1 0.1 0.1 0.5 m
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