Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns

Frequent checks on livestock\u2019s body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health\u2019s anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals\u2019 body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow\u2019s growth through a three-dimensional analysis of their bodies\u2019 portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect\u2122 v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coef\ufb01cients of determination R2> 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R2 = 0.74) and back slope (R2 = 0.12)

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of cross- entropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

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

Analisa Sistem Pemindai 3d Berbasis Kinect Technology pada Komponen Mesin Pertanian

Penelitian ini bertujuan untuk melakukan analisa terhadap sistem pemindai 3D untuk komponen mesin peralatan pertanian menggunakan teknologi kinect. Pemanfaat teknologi kinect ini diharapkan mampu memindai dan mengambil gambar komponen-komponen mesin pertanian secara 3D. Analisa yang digunakan adalah untuk mengetahui tingkat akurasi kinect dalam memindai komponen mesin pertanian. Pemindai 3D ini menggunakan teknologi kinect sebagai sesor utama. Kinect terdiri dari sensor infra red, sensor depth image coms dan sensor color image CMOS. Semua sensor kinect memindai obyek secara 3D dan kemudian diproses oleh processor kinect. Hasil proses tersebut dilanjutkan ke perangkat lunak pada pemroses data sehingga mengasilkan file hasil pemindaian dalam bentuk 3D. Hasil pengujian pada komponen dengan ukuran yang berbeda-beda menunjukan bahwa komponen dengan ukuran dibawah diameter 7,5 dan tinggi 10 cm tidak mampu dipindai. Komponen dengan ukuran diatas diameter 10 cm dan tinggi 15 cm dapat dipindai dengan akurasi yang cukup baik

Classifying Agricultural Terrain for Machinery Traversability Purposes

The detection of the type of soil surface where a robotic vehicle is navigating on is an important issue for performing several agricultural tasks. Satisfactory results in activities such as seeding, plowing, fertilizing, among others depend on a correct identification of the vehicle environment, specially its contact interface with the ground. In the this work, the implementation of a supervised image texture classifier to recognize five different classes of typical agricultural soil surfaces is presented and analysed. The sensing device is the Microsoft Kinect for Windows V2, which allows to acquire RGB, IR and depth data. Only IR and depth data were used for the processing, since color information becomes unreliable under different illumination conditions. Two data acquisition modes allowed to validate and to apply the system in real operation conditions. The accuracy of the classifier was assessed under different configuration parameters, obtaining up to 93 percent of success rate, in ideal conditions. Real field conditions were simulated by placing the sensor over a moving wagon, obtaining up to 86 percent of success rate, showing in this way the usability of a low cost sensor such as the Kinect V2 for agricultural robotics

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