Detection of immovable objects on visually impaired people walking aids

One consequence of a visually impaired (blind) person is a lack of ability in the activities related to the orientation and mobility. Blind person uses a stick as a tool to know the objects that surround him/her.The objective of this research is to develop a tool for blind person which is able to recognize what object in front of him/her when he/she is walking. An attached camera will obtain an image of an object which is then processed using template matching method to identify and trace the image of the object. After getting the image of the object, furthermore calculate and compare it with the data training. The output is produced in the form of sound that in accordance with the object. The result of this research is that the best slope and distance for the template matching method to properly detect silent objects is 90 degrees and 2 meters

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 outputs 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

Cucumber Detection For Precision Agriculture Applications

The objective of this research was to explore the feasibility of detecting cucumber fruits in field conditions for autonomous robotic harvesting applications. A high resolution colour camera and a time-of-flight camera are proposed as primary sensors for the design of the sensory system. The preliminary detection algorithm includes a pixel-based classifier that labels areas of interest that belong to cucumber fruits and a registration procedure that combines the results of the aforementioned classifier with the range data provided by the time-of-flight camera. The detection algorithm is extremely simple and efficient, and provides a satisfactory discrimination of the cucumbers fruits with respect to the rest of the elements of the scene. Several experimental tests have been carried out in outdoor conditions in order to evaluate and demonstrate the capabilities of the proposed approach

A fully automated three-stage procedure for spatio-temporal leaf segmentation with regard to the B-spline-based phenotyping of cucumber plants

Plant phenotyping deals with the metrological acquisition of plants in order to investigate the impact of environmental factors and a plant’s genotype on its appearance. Phenotyping methods that are used as standard in crop science are often invasive or even destructive. Due to the increase of automation within geodetic measurement systems and with the development of quasi-continuous measurement techniques, geodetic techniques are perfectly suitable for performing automated and non-invasive phenotyping and, hence, are an alternative to standard phenotyping methods. In this contribution, sequentially acquired point clouds of cucumber plants are used to determine the plants’ phenotypes in terms of their leaf areas. The focus of this contribution is on the spatio-temporal segmentation of the acquired point clouds, which automatically groups and tracks those sub point clouds that describe the same leaf. The application on example data sets reveals a successful segmentation of 93% of the leafs. Afterwards, the segmented leaves are approximated by means of B-spline surfaces, which provide the basis for the subsequent determination of the leaf areas. In order to validate the results, the determined leaf areas are compared to results obtained by means of standard methods used in crop science. The investigations reveal consistency of the results with maximal deviations in the determined leaf areas of up to 5

PlantES: A plant electrophysiological multi-source data online analysis and sharing platform

At present, plant electrophysiological data volumes and complexity are increasing rapidly. It causes the demand for efficient management of big data, data sharing among research groups, and fast analysis. In this paper, we proposed PlantES (Plant Electrophysiological Data Sharing), a distributed computing-based prototype system that can be used to store, manage, visualize, analyze, and share plant electrophysiological data. We deliberately designed a storage schema to manage the multi-source plant electrophysiological data by integrating distributed storage systems HDFS and HBase to access all kinds of files efficiently. To improve the online analysis efficiency, parallel computing algorithms on Spark were proposed and implemented, e.g., plant electrical signals extraction method, the adaptive derivative threshold algorithm, and template matching algorithm. The experimental results indicated that Spark efficiently improves the online analysis. Meanwhile, the online visualization and sharing of multiple types of data in the web browser were implemented. Our prototype platform provides a solution for web-based sharing and analysis of plant electrophysiological multi-source data and improves the comprehension of plant electrical signals from a systemic perspective

Strawberry detection using a heterogeneous multi-processor platform

Over the last few years, the number of precision farming projects has increased specifically in harvesting robots and many of which have made continued progress from identifying crops to grasping the desired fruit or vegetable. One of the most common issues found in precision farming projects is that successful application is heavily dependent not just on identifying the fruit but also on ensuring that localisation allows for accurate navigation. These issues become significant factors when the robot is not operating in a prearranged environment, or when vegetation becomes too thick, thus covering crop. Moreover, running a state-of-the-art deep learning algorithm on an embedded platform is also very challenging, resulting most of the times in low frame rates. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convo-lutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3% over the test set comprised of 146 images

Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

Human activity recognition for pervasive interaction

PhD ThesisThis thesis addresses the challenge of computing food preparation context in the kitchen. The automatic recognition of fine-grained human activities and food ingredients is realized through pervasive sensing which we achieve by instrumenting kitchen objects such as knives, spoons, and chopping boards with sensors. Context recognition in the kitchen lies at the heart of a broad range of real-world applications. In particular, activity and food ingredient recognition in the kitchen is an essential component for situated services such as automatic prompting services for cognitively impaired kitchen users and digital situated support for healthier eating interventions. Previous works, however, have addressed the activity recognition problem by exploring high-level-human activities using wearable sensing (i.e. worn sensors on human body) or using technologies that raise privacy concerns (i.e. computer vision). Although such approaches have yielded significant results for a number of activity recognition problems, they are not applicable to our domain of investigation, for which we argue that the technology itself must be genuinely “invisible”, thereby allowing users to perform their activities in a completely natural manner. In this thesis we describe the development of pervasive sensing technologies and algorithms for finegrained human activity and food ingredient recognition in the kitchen. After reviewing previous work on food and activity recognition we present three systems that constitute increasingly sophisticated approaches to the challenge of kitchen context recognition. Two of these systems, Slice&Dice and Classbased Threshold Dynamic Time Warping (CBT-DTW), recognize fine-grained food preparation activities. Slice&Dice is a proof-of-concept application, whereas CBT-DTW is a real-time application that also addresses the problem of recognising unknown activities. The final system, KitchenSense is a real-time context recognition framework that deals with the recognition of a more complex set of activities, and includes the recognition of food ingredients and events in the kitchen. For each system, we describe the prototyping of pervasive sensing technologies, algorithms, as well as real-world experiments and empirical evaluations that validate the proposed solutions.Vietnamese government’s 322 project, executed by the Vietnamese Ministry of Education and Training