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.