Fruit Phantoms for Robotic Harvesting Trials—Mango Example (original) (raw)
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Robotic Harvesting of Fruiting Vegetables
2018
In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control a...
Sensors, 2014
The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.
2014
The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the setup , increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.
Development of an Autonomous Multifunctional Fruits Harvester
Open Access Journal of Agricultural Research
There is global shortage of food production due to inadequate workforce, to carry out crop production. This study strived to develop an autonomous system, which can harvest more than one type of crop with high accuracy to improve effectiveness, and minimized production and operational costs. To minimize the mechanical damage done to the fruits during the harvesting and handling operations, the system combined the mechanical properties and optical properties of the fruits during operation. Sensor was attached to the fruits collection container system to prevent overloading of the container, hence protecting the fruits from mechanical stresses. Results obtained for the laboratory trial revealed that the prototype robotic system had a higher performance (about 90%) when tested with pepper fruits, when compared with the performance rating (about 85%) recorded with eggplant fruits. It was also observed from the laboratory trials that the robotic system aborted the harvesting operation, w...
Review on “ Automation in Fruit Harvesting ”
2015
The challenges in developing a fruit harvesting robot are recognizing the fruit in the foliage and detaching the fruit from the tree without damaging both the fruit and the tree. In large-scale greenhouse production, technological developments can reduce production costs; mechanization of crop maintenance and harvesting is one desirable way to accomplish this. Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The objectives of this study were to develop a real-time fruit detection system using machine vision and laser ranging sensor and to develop an end effector capable of detaching the fruit similar to the human picker. This paper deals with fruit recognition and it presents the development of a various techniques for the harvesting of fruits.
IEEE Access, 2021
Modern commercial orchards are increasingly adopting trees of SNAP architectures (Simple, Narrow, Accessible, and Productive) as the fruits on such trees are, in general, more easily reachable by human or robotic harvesters. This article presents a methodology that utilizes three dimensional (3D) digitized computer models of high-density pear and cling-peach trees, and fruit positions to quantify the linear fruit reachability (LFR) of such trees, i.e., their reachability by telescoping robot arms. Robot-canopy non-interference geometric constraints were introduced in the simulator, to determine the closest position of the arms' base frames with respect to the trees, inside an orchard row. Also, design constraints for such arms, such as maximum reach, size and type of the gripper, and range of approach directions, were introduced to estimate the effect of each of these constraints on the LFR. Simulations results showed that 85.5% of pears were reachable after harvesting consecutively, at three different approach angles ('passes') with a gripper of size 11 cm and an arm extension of 150 cm. For peaches, after three passes, 83.5% were reachable with a gripper size of 11 cm and an arm extension of 200 cm. LFR increased as the gripper's size approached the maximum fruit size and decreased thereafter. Also, retractive grippers on linear arms yielded more fruit compared to vacuum-tube type grippers. Overall, the results suggested that telescoping arms can be used to harvest certain types of SNAP-style trees. Also, this methodology can be used to guide the design of robotic harvesters, as well as the canopy management practices of fruit trees.
Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB
In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose output were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish and subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.
Development of an Experimental Strawberry Harvesting Robotic System
Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
This paper presents the development of an integrated strawberry harvesting robotic system tested in lab conditions in order to contribute to the automation of strawberry harvesting. The developed system consists of three main subsystems; the vision system, the manipulator and the gripper. The procedure for the strawberry identification and localization based on vision is presented in detail. The performance of the robotic system is assessed by the results of experiments that take place in the lab and they are related to the recognition of occluded strawberries, the check of the strawberries for possible bruises after the grasping and the accuracy of detection of the strawberries' location. The results show that the developed vision algorithm recognizes correctly every single strawberry and has high accuracy in recognizing occluded strawberries in which the largest part of each of them is visible. A small localization error results in a correct grasp and cut without causing damage to the fruit.
An end-effector for spherical fruit harvesting robot was developed. This end-effector is a multi-sensory one that is universal for spherical fruit such as tomatoes, apples and citrus. It performs fruit singulation with a vacuum suction pad device, fruit gripping and peduncle locating with a two-finger (an upper finger and a lower finger) gripper and peduncle cutting with a laser cutting device. In order to percept sufficient information of the internal state, harvesting object and environment, different types of sensors are configured, including a vacuum pressure sensor, distance sensors, proximity sensors and force sensors. An open architecture of control system based on IL+DSP is adopted, which is more open, flexible, universal and lighter to be more suitable for a mobile harvesting robot and end-effector.