OpenScout: Open Source Hardware Mobile Robot

In this paper, we propose an implementation method for a hardware-in-the-loop (HIL) simulator for an inverted pendulum system using low cost open-source hardware. We adopted the Arch Max (ARM Cortex-M4-based open-source hardware) as an HIL simulator platform. Using the nested vectored interrupt controller (NVIC) mechanism of the Cortex-M4 core, we assigned three interrupts with different priorities to handle three parts of the HIL simulator. The model equation of the inverted pendulum system was solved in real-time using the Runge-Kutta method in a timer interrupt service routine. An input capture interrupt-based method was used to simulate the motor drive by converting the duty ratio of pulse width modulation (PWM) signals into the voltage level applied to a DC motor. To simulate incremental rotary encoders, which are two sensors for the inverted pendulum system, a timer interrupt-based quadrature pulse generation algorithm was presented and to generate the quadrature pulse more accurately, an error compensation method was also provided. Finally, animation functionality to give a visual indication of how the controller is progressing was implemented through Vpython. Through experiments, we illustrated that the proposed HIL simulator successfully simulates the real inverted pendulum and is useful for teaching students about designing a working controller through programming embedded devices.

This chapter explores open source hardware. Open source hardware is a relative newcomer in comparison with open source software. As a field it has adopted many assumptions and models from the world of open source software, but its latecomer status gives it the advantage of being able to learn from what has gone before. At this juncture, there are far fewer businesses operating in the world of open hardware, and those businesses are less prominent and successful (although there are signs that this is rapidly changing). There are also far fewer specific open hardware licences than open source software licences, and there are still fundamental questions about the applicability of various different forms of intellectual property right to different aspects of hardware.

Dendrochronological techniques are paramount in forest research. The current climate change scenario and the central role of forests in biogeophysical cycles enforce the importance of novel techniques to get accurate data from trees and their relationship with the environment in faster ways. Recent technological advances and the place of open source software and hardware are making free, user-developed tools for forest research available to the research community. The aim of this Ph.D. thesis is the development of tools for image acquisition and data collection in dendrochronology based on open source software and hardware. Thus, four different tools for dendrochronological research are presented in five different chapters. The first chapter focuses on the development of a do-it-yourself tool based on open source hardware for image acquisition and wood sample digitization at high resolution. We used open hardware equipment from Arduino and Python programming to develop CaptuRING and published the entire free open source tool as: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" in Methods in Ecology and Evolution, 2022; 13:1185-1191. Furthermore, the original software was registered in the Registro General de Propiedad Intelectual (00/2022/737) of Ministerio de Cultura y Deporte (Spain). The second chapter presents "How to build and install your own CaptuRING". This contribution presents a series of videos with a step-by-step guide to promote the use of CaptuRING in the research community. The manuscript and related videos have been submitted for publication. The third chapter describes ρ-MtreeRing. This free and open-source software, which is written in R, analyzes X-ray films from dendrochronological samples to get microdensity values automatically segmented through a graphical user interface. The open source tool and manuscript are published as: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" in Forests. 2021; 12(10):1405. The fourth chapter describes the potential of deep learning methods to automatically segment xylem vessels. We trained three different convolutional neural networks to segment vessels in stained wood microsections using the Keras framework in Python. Our results demonstrate the potential of these techniques to automatically segment xylem vessels and overcome derived problems from image illumination, which hamper segmentation using classical image segmentation methods. The manuscript is published as "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" in: Neural Computing & Applications, 2020; 32:17927-17939. The fifth chapter develops an algorithm to delineate annual ring limits in stained wood microsections of a species with diffuse porous wood using convolutional neural networks. We used Python for image processing and the Keras framework for the algorithm training. The results show the ability of this techniques to obtain accurate tree ring segmentation for quantitative wood anatomy, reaching similar or even outperforming conventional manual delimitation in most of the evaluated cases. The results of this chapter will be presented in the manuscript "Deep Learning for ring bordering in stained cross-sectional images". This PhD Thesis presents four open source tools to get accurate information from wood features to unveil how trees respond to the environment. From digitization at macroscopic perspective, automatic data collection and the development of feature segmentation on microscopic samples. The presented four novel dendrochronological tools based on open source software facilitates forest research in the current climate change scenario.

Open source hardware is a type of hardware where the schematics and designs are made unrestricted and available to all. They are often accompanied by open source software. This can bring reliability, ease of debugging, and modular development for rapid prototyping using pre-written libraries. Merits of using open source hardware are discussed and then applied to a portable sensor system based on the open hardware Arduino-derived Jeenode microcontroller board. The system uses an inertial measurement unit (IMU) and seamless integration of other sensors, including a piezo-resistive pressure sensor. It is shown that open source hardware can help to increase rapid development, reduce costs, and encourage further development.

The advent of easy-to-use open source microcontrollers, off-the-shelf electronics and customizable manufacturing technologies has facilitated the development of inexpensive scientific devices and laboratory equipment. In this study, we describe an imaging system that integrates low-cost and open-source hardware, software and genetic resources. The multi-fluorescence imaging system consists of readily available 470 nm LEDs, a Raspberry Pi camera and a set of filters made with low cost acrylics. This device allows imaging in scales ranging from single colonies to entire plates. We developed a set of genetic components (e.g. promoters, coding sequences, terminators) and vectors following the standard framework of Golden Gate, which allowed the fabrication of genetic constructs in a combinatorial, low cost and robust manner. In order to provide simultaneous imaging of multiple wavelength signals, we screened a series of long stokes shift fluorescent proteins that could be combined with cyan/green fluorescent proteins. We found CyOFP1, mBeRFP and sfGFP to be the most compatible set for 3-channel fluorescent imaging. We developed open source Python code to operate the hardware to run time-lapse experiments with automated control of illumination and camera and a Python module to analyze data and extract meaningful biological information. To demonstrate the potential application of this integral system, we tested its performance on a diverse range of imaging assays often used in disciplines such as microbial ecology, microbiology and synthetic biology. We also assessed its potential use in a high school environment to teach biology, hardware design, optics, and programming. Together, these results demonstrate the successful integration of open source hardware, software, genetic resources and customizable manufacturing to obtain a powerful, low cost and robust system for education, scientific research and bioengineering. All the resources developed here are available under open source licenses.

We have attempted to create affordable programmable logical controllers (PLC) that can be used to train millions of students in developing countries. Version 1 of the PLC is based on AtMega16 and costs 600 ($10). Using the free and open source software (FOSS) LD Micro, one can learn ladder logic. To enhance the capabilities, using Raspberry PI, Version 2 of the PLC, costing 1,500 ($25) is made. We can use this device with the FOSS OpenPLC to learn ladder logic. We can also learn function block based programming using the FOSS 4DIAC. As ladder logic and function block based programming are industrial standards, students trained using our approach will be useful to industry.

3-D printing technologies have become widely adopted and have spurred innovation and efficiency across many markets. A large contributor to the success of 3-D printing are open source, low cost electronics. On-site circuit manufacturing, however, has not become as widely utilized as 3-D printing. This project attempts to address this problem by proposing and demonstrating an open source circuit board milling machine which is inexpensive, easily manufactured, and accurate. In three interdependent sub-projects, this thesis defines a standard method for designing open source hardware, the design of the bespoke circuit mill, and explores an application of the mill for novel circuit manufacturing. The first sub-project develops a standardized process for designing, prototyping, and distributing open source hardware. Following these steps can help ensure success for each individual part of the project. In order to validate the procedure, a case study is explored of designing low cost parametric glass slide driers. The second sub-project details the design and construction of a circuit prototyping machine. The open source design procedure is implemented to assure maximum effectiveness. A software interface is also designed to control and carry out processing steps on the milling machine. The mill minimizes lead time and production costs of experimental circuitry. The mill also stands as a strong open source tool that can help foster growth in distributed manufacturing of electronics for a wide array of applications. The third and final sub-project explores a flexible and scalable power monitoring system. The electronics are designed according to the open source design procedure and are manufacturable with the circuit milling machine. The power meter can be used to monitor and log power consumption of a wide range of loads, including both AC and DC.

Mobile robots have the potential to be used in many outdoor tasks, such as search and rescue, patrolling, and delivery services. To enable robots to safely navigate through outdoor environments, it is important to analyse the terrain. We present a novel approach using Semantic Terrain Segmentation (STS), which relies on an adaptation of Deeplabv3+. Our goal is to segment the terrain according to different height thresholds, using only aerial RGB images. We collected and labelled a dataset, Vale, consisting of four categories based on the traversability constraints of three types of mobile robots: wheeled, tracked, legged and non-traversable. We trained our deep convolutional neural network (DCNN) on Vale, with transfer learning using a network pre-trained on Cityscapes. Our findings suggest that the proposed DCNN approach can identify and differentiate different height thresholds in the terrain, i.e. can segment based on mobile robot traversability.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. You've heard about open source software, but what is open source hardware? Come hear experts from across the industry and in academia discuss the new face of open source, Hardware IP: - What is it? - How are companies and academics using it? - Can Open Source Hardware significantly change the design world?

We describe the application of a self-organizing network to the robot which learns to recognize rooms (enclosures) using behavior sequences. In robotics research, most studies on recognizing environments have tried to build the precise geometric map with highly sensitive sensors. However many natural agents like animals recognize the environments with low sensitivity sensors, and a geometric map may not be necessary. Thus we attempt to build a mobile robot using a self-organizing network to recognize the enclosures, in which it acts, with low sensitivity and local sensors. The mobile robot is behavior-based and does wall-following in an enclosure. Then the sequences of behaviors executed in each enclosure are obtained. The sequences are transformed into real-value vectors, and inputted to the Kohonen self-organizing network. Unsupervised learning is done and a mobile robot becomes able to distinguish and identify enclosures. We fully implemented the system using a real mobile robot and made experiments for evaluating the ability. Consequently we found out the recognition of enclosures was done well and our method was robust against small obstacles in an enclosure.

Design and construction of an automated OSL reader with open source software and hardware

Objective To understand the prevalence of thyroid nodules of children lived in different water iodine areas in Cangzhou City. Methods From Oct. 2015 to Jan. 2017, 15 villages were selected as monitoring sites in Cangzhou, two drinking water samples were collected from each survey site (all had centralized water supply), and the water iodine content was determined. A total of 100 children aged 8 to 10 (half male and female) were examined for thyroid nodules, and at least 50 children (half male and half female) were selected to detect urinary iodine content. In the high iodine water counties, the monitoring sites of iodine salt was according to National Iodine Deficiency Monitoring Program; in the monitoring sites of iodine salt supplied counties, students in the monitored village were asked to detect urinary iodine and household salt samples were collected to monitor salt iodine. In the high iodine area, the salt iodine test was carried out by semi-quantitative method. In the non-high iodine area, the salt iodine content of the iodized salt monitoring sites was determined by direct titration, the salt iodine content of Chuan salt and other intensified edible salt was tested by arbitration (GB/T 13025.7-2012). Water iodine and urinary iodine were tested by arsenic and cerium catalytic spectrophotometry. Results Water iodine content was 28.2-1 128.0 μg/L in 15 villages; a total of 1 066 urine samples were examined, the median of uriary iodine in each village was 102.6-1 162.0 μg/L; a total of 1 575 children aged 8 to 10 years were examined, among them, 125 cases of thyroid nodules were detected; thyroid nodules detection rate was 7.9%. The prevalence of male was 7.0% (61/871), and the prevalence of female was 9.1% (64/704), there was no significant difference in the detection rate of thyroid nodules between different sex (χ2= 2.07, P > 0.05); The detection rate of thyroid nodules were 4.5% (23/508), 7.8% (4/51), 11.6% (59/507) in children with urinary iodine at the appropriate level (100-< 200 μg/L), the appropriate level (200-< 300 μg/L) and iodine excess level (≥300 μg/L), the difference of thyroid nodules in children with different levels of urinary iodine detection rate was statistically significant (χ2= 17.30, P < 0.01). The difference of prevalence of thyroid nodules in children aged 8 to 10 years with water iodine concentrations of 10-< 100, 100-< 300 and ≥300 μg/L was statistically significant [2.9% (13/448), 7.9% (25/317), 10.7% (87/810), χ2= 23.86, P < 0.05]. The patients with unilateral thyroid nodule accounted for 64.8% (81/125); the patients with multiple thyroid nodules counted for 58.4% (73/125), and 34.2% (13/38), 69.0% (60/87) in areas with iodine content less than 300 μg/L and no less than 300 μg/L, the difference between the two was statistically significant (χ2= 13.14, P < 0.01). A total of 1 800 salt samples were collected from the high water iodine counties, of which 1 779 were iodine-free salt, the rate of iodine-free salt was 98.8%; a total of 190 salt samples were collected in student family, in the 4 iodized salt monitoring sites, the salt iodine median of resident's edible salt was 0.0 mg/kg. Conclusion The prevalence of thyroid nodules in children aged 8-10 years may be related to high water iodine in Cangzhou City; children with multiple thyroid nodules is also significantly higher in water iodine content greater than 300 μg/L areas. Key words: Thyroid nodules; Water; Urine; Iodine

The importance of intangible code modularity in open-source software, as well as of tangible product modularity in proprietary hardware, is widely acknowledged. Nevertheless, modularity in open-source hardware (OSH) remains under-researched. This article first describes qualitatively different types of modularity based on two OSH case studies and then quantifies each type of modularity, following a unified network-based approach. The results are discussed and compared within each case to test the ‘mirroring hypothesis’, and between cases to evaluate the impact of physical against intangible modularity types. The ultimate goal is to prompt a discussion into a wide but under-explored subset in OSH.

Context. The SLAM problem solving for an autonomous mobile robot requires efficient technical means for surrounding terrain scanning with the purpose of its mapping and obstacles detecting. As such means, laser scanning systems are widely used both independently and in combination with other tools as a part of a uniform technical vision system of a mobile robot. One of the problems with a technical vision systems operation is the ability to detect relatively small obstacles, which requires scanning of a limited sector within the field of view or even focusing on a specific point of space. This study is devoted to the issue of improving the reliability of obstacles detecting within the robot field of view by improving the laser actuator of the technical vision system. Objective. The objective of the work is to the reliability increasing of obstacles detection within the robot technical vision system field of view due to the robust control of the actuators of the positioning system of the laser. Method. The classical PD-controller for the laser positioning actuator, which meets the requirements for the quality of the transient process, has been synthesized. The evaluation of robust properties of the obtained control system showed both a significant dependence of the actuator dynamic properties on the variation of the values of the actuator model parameters and the potential possibility of its time response increasing. With the help of the MATLAB Robust Control Toolbox, the controller gains have been redefined to ensure the robust properties of the control system. The analysis of the influence of parametric uncertainties of the actuator model on the system dynamic properties is conducted. Results. A robust PD-controller for the laser beam positioning system actuator for the technical vision system has been synthesized. It combines the simplicity of the implementation of a classic controller with a weak sensitivity to the uncertainties presence. Conclusions. The using of the controller at the mobile robot laser technical vision system will increase the reliability of obstacle detection and, as a result, the accuracy of mapping of a limited sector within the robot field of view under the conditions of the parametric uncertainty of the actuator model.

Assistance robots are rising as a promising solution to help workers in everyday life, for many applications. In particular, many outdoor tasks, such as manual harvesting or carrying heavy loads, may benefit from the use of a mobile robot following a human worker. The literature offers various frameworks and systems for target tracking, using several kinds of sensors and control algorithms. Most of them are based on vision, which is sensitive to lighting conditions, or LIDAR. Both of these sensors require to keep the human in line of sight to be tracked, and have limitations in some challenging environmental conditions encountered outdoors, such as in rough weather or in the presence of airborne dust or fog. Moreover, existing approaches are often focused on direct tracking and may lead the robot to cross the human trajectory. This is penalising when considering off-road assistance, especially in agriculture, where the environment is often narrow (because of crop rows), and require many obstacle avoidance situations. In this paper an algorithm based on Ultra-Wide-Band technology is proposed in order to ensure a relative localisation without the need for a direct view of the target. The method approximates locally the trajectory of the human leader as a circle in order to follow the path achieved by the human, without direct communication nor absolute localization system. This is made possible thanks to the on-line reconstruction of the leader's variables (namely, its velocity and angular course), using a state observer. The effectiveness of this adaptive approach is demonstrated through full-scale experiments.