论文标题
用于机器人水产养殖监测的路径规划算法
Path Planning Algorithms for Robotic Aquaculture Monitoring
论文作者
论文摘要
空中无人机具有快速有效监视大面积的巨大潜力。水产养殖是一个需要连续水质数据才能成功种植和收获鱼类的行业。混合空中水下机器人系统（HAUCS）旨在收集水产养殖池的水质数据，以降低农民的人工成本。无人机在水产养殖场上覆盖每个鱼池的路线可以减少到车辆路线问题。创建了一个数据集来模拟农场上池塘的分布，并用于评估HAUCS路径计划算法（HPP）。将其性能与Google线性优化软件包（GLOP）和用于路由问题的图形注意模型（AM）进行了比较。 GLOP是50至200个池塘的最有效的求解器，而长期运行时间为代价，而HPP在溶液质量方面的表现优于其他方法，并为大于200个池塘的实例运行时间。
Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (AM) for routing problems. GLOP is the most efficient solver for 50 to 200 ponds at the expense of long run times, while HPP outperforms the other methods in solution quality and run time for instances larger than 200 ponds.
