The autonomous grape vine pruning robot being developed by the Computer Vision Research Group at the University of Canterbury will be capable of imaging vines to create a 3D model of them, navigating around orchards, and pruning vines with a high degree of freedom robotic arm. This thesis develops the system to plan and execute motions for the robotic arm. Path planning for robotic arms involves finding a sequence of joint positions in the robot’s configuration space that can be moved through to create a motion in Cartesian space that avoids any collisions and gets the end effector of the robot, in this case a pruning tool, to a goal position and orientation. Because of the infeasibility of representing the grape vine as an obstacle in the robot’s configuration space, sampling based path planning algorithms that only collision check specific randomly sampled joint positions offer the best approach.

Robotics Operating System (ROS) is used as a framework that enables development with a simulated robot and operation of the real robot. The six degree of freedom UF850 by Ufactory is selected with its suitability to the application verified through simulating path planning on 30 synthetic vines, where for all vines a valid pruning strategy given the points the arm could reach can be determined. An implementation method where single paths are found between cut points and a safe plane offset from the vines is proposed to minimise the distance traversed by the planning algorithm and to configure the problem in a way that leads to high quality solutions. The performance of 10 different path planning algorithms with different features from the literature is tested. Following a successful extensive evaluation, Informed RRT* is the selected algorithm with an average length of individual paths of 1.88 seconds and 81.9% of possible cut points reachable.

The proposed implementation to prune whole vines maximises the quality of the paths found by using all of the time spent executing one cut to plan the next. While operating the real UF850 it is able to successfully execute a realistic pruning strategy on all of the 20 vines tested. It does so with an average time per cut of 10.85 seconds and an average time per vine of 139.3 seconds. These results demonstrate that once integrated with the full rover the system developed provides an effective approach to autonomous grape vine pruning.