Vision-based control of robotic manipulator for citrus harvesting

Abstract:In this paper, we present a recent survey on robotic grippers. In many cases, modern grippers outperform their older counterparts which are now stronger, more repeatable, and faster. Technological advancements have also attributed to the development of gripping various objects. This includes soft fabrics, microelectromechanical systems, and synthetic sheets. In addition, newer materials are being used to improve functionality of grippers, which include piezoelectric, shape memory alloys, smart fluids, carbon fiber, and many more. This paper covers the very first robotic gripper to the newest developments in grasping methods. Unlike other survey papers, we focus on the applications of robotic grippers in industrial, medical, for fragile objects and soft fabrics grippers. We report on new advancements on grasping mechanisms and discuss their behavior for different purposes. Finally, we present the future trends of grippers in terms of flexibility and performance and their vital applications in emerging areas of robotic surgery, industrial assembly, space exploration, and micromanipulation. These advancements will provide a future outlook on the new trends in robotic grippers.

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“…In [64], a citrus harvester was also designed with a large field-of-view camera. Similar designs were presented in [65,66].…”

Section: Grippers For Fragile Objectsmentioning

confidence: 99%

State of the Art Robotic Grippers and Applications

et al. 2016

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“…They achieved 90% detection accuracy with a 4% false positive rate. More recently, Mehta and Burks (2014) developed a vision-based fruit depth estimation and robotic harvesting system using a computationally efficient method and in-depth visual servo control formulation.…”

Section: Robotics For Citrus Harvestingmentioning

confidence: 99%

“…Table 1 Reference Achievement Robotics for citrus harvesting Harrell et al (1985) One of the first applications for harvesting Moltó et al (1992) Utilised the differences in the reflectance spectrum and reflection patterns to locate the fruit in the trees Burks et al (2003) Reported that field conditions, plant population and spacing, and plant shape and size were the most important factors for mechanical harvesting in the horticultural aspect Flood et al (2006) Studied a maximum value for an end-effector grasping force for harvesting Subramanian et al (2006) Developed an autonomous guidance system for citrus grove navigation using machine vision and laser radar Hannan et al (2009) Developed a machine vision algorithm based on red chromaticity coefficient to identify oranges for robotic harvesting. Mehta and Burks (2014) Developed a vision-based fruit depth estimation and robotic harvesting system using in-depth visual servo control formulation. HLB detection Developed several HLB detection algorithms from airborne spectral images of citrus groves Garcia-Ruiz et al (2013) Compared images taken from an aircraft with those acquired by a UAV to detect HLB Li et al (2014) Developed a novel algorithm, called extended spectral angle mapping or ESAM to detect the presence of HLB Li et al (2015) Compared satellite images to aerial images in order to detect HLB Pourreza et al (2015a) Developed a handheld HLB detection system using starch accumulation on infected leaves Pourreza et al (2015b) Reported that the vision sensor system developed was able to separate HLB infection from zinc deficient leaves Choi et al (2015) Developed a machine vision system to detect fruit that had been dropped on the ground due to HLB Yield prediction Annamalai and Lee (2003) Developed an algorithm to detect citrus fruits on trees using hue and saturation thresholds of citrus fruit, leaves, and background classes Annamalai and Lee (2004) Investigated spectral signatures of immature green citrus fruits and leaves to develop spectralbased fruit identification and an early yield mapping system Ye et al (2008) Utilised two-band vegetation index to develop yield prediction models, canopy size, and both of them together Okamoto and Lee (2009) Developed ground-based detection algorithms to identify green immature citrus for three different varieties using the VIS/NIR range.…”

Section: Inspection Of Fruit In the Field Using Mobile Platformsmentioning

confidence: 99%

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

102

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Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.

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“…Models in V-REP are flexible, portable and scalable, meaning that it is possible to modify them, copy from one project scene to another, or resize them in place. If the project requires building a custom robot model which is not available in the simulator (i.e., the manipulators demonstrated in [1,34,35] ), the setups for links, joints and calculation modules such as inverse kinematics necessitates some practice, however, that is the case in any robot simulation software. …”

Section: Virtual Robot Experimentation Platform (V-rep)mentioning

confidence: 99%

Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison

Shamshiri

Hameed

Pitonakova

et al. 2018

International Journal of Agricultural and Biological Engineering

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Abstract:Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade. Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments. With the exception of milking robots, the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date. To accelerate this pace, simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios. This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications. The key features and performance efficiency of three selected simulators were also compared. A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform. Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard. It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces, algorithms, and reusing the materials.

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Vision-based control of robotic manipulator for citrus harvesting

Cited by 192 publications

References 15 publications

State of the Art Robotic Grippers and Applications

State of the Art Robotic Grippers and Applications

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison

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