Determining the Optimal Route for a Tethered Manure Applying Robot

Abstract

Lely is developing a tethered manure applicator robot, designed to work on the fields of farms. This vehicle called the Jojo, is constrained in its movement due to the attached tether. It can not make tight turns and can only reverse by backtracking the driven path. This thesis explores the path planning issue and provides a novel solution. The problem is best described as the shortest path from a starting point to a set of strokes that cover the field, with a constrained turning radius. We first explore existing solutions and discover that none exist that are suitable. Further research finds that the behaviour of this vehicle is best compared with steerable needles, but at a different scale. First, we construct a simulation environment for the vehicle. This simulation is used to develop the plan execution engine, path follower and safety checks. This is necessary for testing the solutions in the simulation and in the real world. Secondly, with some inspiration from the solutions provided for the steerable needles, we arrive at a method for finding a Jojo path. The method makes use of a large graph that represents translations associated with a given location. We also provide methods for attaching the starting point and destinations in the form of strokes, either via the start or end of the strokes or via the middle. A Jojo plan is then obtained by performing a directed Steiner tree approximation on this graph, where the starting point is the root and the terminals are the strokes. In order to make the approach fast enough, we spend a substantial amount of time profiling and analysing the program and design a number of performance improvements. These allow for the approach to solve instances that are larger than necessary. Thirdly, we test, compare and improve the approach. We look at both small instances to perform numerical analysis in order to optimise a set of parameters. We then inspect larger instances that are based on real world scenarios. Lastly, we perform a set of real world tests using the vehicle. One of these tests highlights a shortcoming of the algorithm, which we discuss and provide a solution for