Emperor Yu Tames the Flood: Water Surface Garbage Cleaning Robot Using Improved A* Algorithm in Dynamic Environments

Abstract

Every year, the global production of marine debris reaches a staggering 400 million tons, and the amount of garbage continues to increase. Among this, about 14 million tons will eventually flow into the oceans, posing a significant threat to marine ecosystems. Apart from endangering marine ecology, this waste will ultimately come back to affect humans. To address the increasingly severe issue of marine debris, our team independently developed a surface garbage cleaning robot, DaYu No. 1, and designed a path planning algorithm specifically for DaYu No. 1, called DyNav. DaYu No. 1 aims to increase garbage cleaning efficiency and adaptability. The traditional A $^{\ast }$ algorithm performs well in static environments, but the ocean is a dynamic environment where marine debris constantly shifts due to ocean currents or tides, making it extremely challenging for robots to collect marine debris. Therefore, our proposed DyNav algorithm can adaptively adjust paths to cope with environmental changes. We proposed DyNav, a new path planning algorithm that takes ocean currents into account, and the DaYu No. 1 robot with YOLOv7 and IMA algorithms built in. These will make cleaning up marine debris much more efficient and flexible. Compared to traditional path planning algorithms, DyNav demonstrates superior performance. We have validated the effectiveness of the DyNav algorithm through practical experiments. The results show that in dynamic environments, the robot can clean surface debris more quickly and flexibly while avoiding obstacles. This not only contributes to environmental protection, but also improves cleaning efficiency, offering an innovative solution for water environment management.

Keywords

• Water surface garbage cleaning robots
• A* algorithm
• path planning
• Greedy
• RRT