Ph.D Thesis

Ph.D StudentBloch Victor
SubjectMethods for Simultaneous Orchard and
Harvesting Robot Design
DepartmentDepartment of Civil and Environmental Engineering
Supervisors ASSOCIATE PROF. Amir Degani
DR. Avital Bechar
Full Thesis textFull thesis text - English Version



Robotic manipulators can perform a variety of agricultural tasks. However, despite decades of research, few agricultural robots have been commercialized. One of the reasons for the lack of agricultural robots on the market today is their high cost, which makes them unprofitable for farmers.

To decrease the cost of robot manipulators for agriculture by improving the robot performance, we propose designing a robot that is optimal for a specific task. In the optimization process, the robot's performance is maximized keeping the ability of the robot to perform the task. To achieve a reliable result, the actual field task must be described and modeled with sufficient precision.

The main goal of this research is to develop methods for simultaneous design of orchard and harvesting robot. The influence and interaction of the orchard with the task-based robot optimal design was analyzed. This analysis allows to simplify the task description by characterizing the environment during the simultaneous design of the robot and its environment. To achieve this goal, the research was conducted to achieve the following specific goals:

         building geometric models of actual agricultural environments

         performing the robot task-based optimization

         developing a methodology for analysis of the agricultural environment, which includes characterization of the environment with the help of our newly formalized concepts of fruit clustering and reaching cones

         testing the fit of the agricultural environment to robotic operation and designing an improved agricultural environment simultaneously with the robot design.

The main results of the research are as follows. For the task-based robot optimization, we created a library with approximately 20 plant models. Based on the model library, we found robots with optimal kinematics for a number of agricultural tasks and environments. During the robot optimization, we found that the level of the complexity of environment does not permit our software to solve the optimization problem in an acceptable time. In addition, a method for optimal robot location was developed.

To solve the robot-optimization problem for complex environments, we developed a method for characterizing the agricultural environment by fruit clustering and reaching cones. The method systematically reduces the complexity of the environments, thereby decreasing the amount of calculations and providing a near-optimal solution. The method was approved and successfully applied to complex environments, solving the optimization problem in hours, rather than after days or weeks of calculations. The expected precision of the achieved solutions was 10% in our case.

We made a preliminary design of the robot working environment. We found an environment that was maximally fitted to the robotic operation and optimized one of the parameters defining the structure of the environment.

In general, we developed a set of tools and methodology for analysis and design of the agricultural environment together with the robot design. This methodology is novel in robot design, particularly in agriculture. The methods developed in this research can be used for robotic harvesting of any type of fruits, for other agricultural tasks or any robotic area, where the robot-environment design is used.