基于FIA*-APF算法的蟹塘投饵船动态路径规划

为了提高无人投饵船在含障碍物河蟹养殖池塘自主巡航的作业效率和安全性，该研究提出基于改进A*算法与人工势场法相融合（fusion of improved A* and artificial potential field，FIA*-APF）的蟹塘投饵船动态路径规划算法。首先引入动态加权因子优化A*算法评价函数；其次加入转折惩罚函数并删除冗余点，接着利用B样条曲线对全局路径进行平滑处理；最后将改进A*算法得到的全局路径作为改进人工势场法中的引力路径，生成投饵船自主巡航高效路径。根据养殖池塘创建静态和动态2种仿真环境，分别对传统人工势场法（traditional artificial potential field，TAPF）、基于A*和人工势场法的融合算法（the A* and artificial potential field，TA*-APF）和FIA*-APF算法的性能进行20次测试。仿真试验结果表明：2种环境下，FIA*-APF算法的平均规划时间是TAPF算法的17.23%，是TA*-APF算法的51.96%，平均指令节点数量比TAPF算法减少50.64%，比TA*-APF算法减少65.03%，平均路径长度比TA*-APF算法减少2.82%。蟹塘试验结果表明：FIA*-APF算法的规划时间为TAPF算法的38.16%，为TA*-APF的62.42%，路径长度比TAPF算法减少29.13%，比TA*-APF减少10.15%；另外，TAPF和TA*-APF算法规划路径上大于60°的转角分别是FIA*-APF算法的3.28和2.62倍，大于100°的转角分别是FIA*-APF算法的3.73和1.67倍，该研究算法规划的路径更高效平滑。研究结果可为无人投饵船自主导航提供参考。

Dynamic path planning for feeding boat in crab pond using FIA*-APF algorithm

Most of the crabs are fed manually at present in China. However, manual feeding cannot fully meet the industrial requirements of mass production, particularly with an increasingly aging labor force. Fortunately, automatic navigation can be expected to promote the development of unmanned feeding boats. This study aims to realize the real-time path planning of an unmanned feeding boat in a crab pond environment with static and dynamic obstacles. A dynamic path planning was proposed for the unmanned feeding boat using the fusion of the improved A* and the artificial potential field (FIA*-APF). Better performance was achieved to solve the excessive turning points, large path curvature, and numerous redundant points in the traditional A* path planning. Firstly, the evaluation function was optimized to find the end of the path. Then, the turn penalty function was introduced to remove the redundant points for the smaller number of turning points. The path was then smoothed using a B-spline curve. Finally, the gravitational and repulsive functions of the artificial potential field were improved to obtain the global paths using the improved A*. The gravitational paths were used to treat the local optimization and unreachability in the artificial potential field. Two aquaculture pond environments with static and dynamic obstacles were established to evaluate the performance of the traditional artificial potential field (TAPF) , the fusion with the A* and artificial potential field (TA*-APF), as well as the FIA*-APF. Planning speed, number of nodes and path length were selected as the evaluating indicators. Each experiment was carried out 20 times in every single environment. The simulation results showed that the average planning time in the two environments was 17.23% and 51.96%, respectively, compared with the TAPF and the TA*-APFs. Furthermore, the average node numbers were 50.64% and 65.03% less than that, respectively. The average path length was 2.82% less than the TA*-APF. The results of 10 crab pond tests showed that the planning time was only 38.16% and 62.42% of the TAPF and the TA*-APFs, respectively. The average path lengths were 29.13% and 10.15% less than that, respectively. In addition, the TAPF and TA*-APFs planned paths with corners greater than 60° were 3.28 and 2.62 times greater than FIA*-APF, while the corners greater than 100° were 3.73 and 1.67 times greater than FIA*-APF, respectively. The path planned was safer and smoother after optimization. The finding can provide a sound reference for the navigation development in unmanned feeding boats.