Path Planning Algorithms for Automated Agricultural Machines Performing Sequentially Dependent Operations in Arable Farming

该项目的重点是为自动化农业机械（包括自动拖拉机）开发计算覆盖路径规划算法。这个项目的主要目标是研究新的算法顺序相关的农业机器人操作的新兴问题。顺序相关意味着一个操作必须在另一个之前完成。这个问题尤其出现在可耕种农业中，其中在耕种周期期间在田地上连续地执行若干覆盖操作。有些操作，如耙和播种，可以立即一个接一个地进行。传统的耕作方式是在整个田地上一次只进行一项操作。几项研究考虑的问题，其中一个单一的覆盖操作，如播种是在多台机器之间划分，和机器同时工作在同一领域的相同操作。典型的解决方案是为每台机器分配自己的工作空间，使它们能够独立工作，而不会发生冲突或碰撞。然而，使机器能够并行执行不同的操作可以减少多个操作的总完成时间，从而提高工作效率。该项目的目标是使多台机器能够在基本相同的土地上同时进行不同的操作，而无需等待整个领域的前一个操作完成。这意味着为每台机器分配各自的静态工作空间的简单解决方案是不可行的。挑战的一个重要部分是机器是异构的，并且通常具有不同的工作宽度。这意味着一台机器不能简单地跟随另一台机器。在这个项目中的工作包括推导顺序相关的机器人问题的形式化定义，推导其算法的解决方案，最后，验证和证明该算法在真实的生活与真实的农业机械的可行性。为了证明结果的实用性，该项目包括对现实生活中的农业设备进行的经验性测试，包括拖拉机和可耕地耕作作业的工具。

This project focuses on developing computational coverage path planning algorithms for automated agricultural machines, including autonomous tractors. The main objective of this project is to study novel algorithms for the emerging problem of sequentially dependent agricultural robotic operations. Sequentially dependent means that one operation must be completed before the other. This problem arises especially in arable farming where several coverage operations are performed consecutively on the field during the cultivation cycle. Some of the operations, such as harrowing and seeding, can be performed immediately one after the other. The conventional way in arable farming is to perform only one operation at a time on the entire field. Several studies consider the problem where a single coverage operation such as seeding is divided among multiple machines, and the machines are working simultaneously on the same operation on the same field. A typical solution is to assign each machine their own workspace, allowing them to work independently without risk of conflicts or collisions. However, enabling the machines to perform different operations in parallel can reduce the total completion time of multiple operations, and therefore, improve the work efficiency. The goal of this project is to enable multiple machines to work on different operations simultaneously on essentially the same area of land without waiting for a previous operation to be finished on the entire field. This means that the simple solution of assigning each machine their individual, static workspace is infeasible. A significant part of the challenge is that the machines are heterogeneous, and oftentimes have different working widths. This means that one machine cannot simply follow another. The work in this project consists of deriving a formal definition of the sequentially dependent robotic problem, deriving an algorithmic solution to thereof, and finally, verifying and demonstrating the feasibility of the algorithm in real life with real agricultural machinery. To demonstrate the practical applicability of the results, this project includes empirical tests with real-life agricultural equipment, including tractors and implements for arable farming operations.