Design and Implementation of Indoor Disinfection Robot System

After the outbreak of COVID-19 virus, disinfection has become one of the important means of epidemic prevention. Traditional manual disinfection can easily cause cross infection problems. Using robots to complete disinfection work can reduce people's social contact and block the spread of viruses. This thesis implements an engineering prototype of a indoor disinfection robot from the perspective of product development, with the amin of using robots to replace manual disinfection operations. The thesis uses disinfection module, control module and navigation module to compose the hardware of the robot. The disinfection module uses ultrasonic atomizers, UV-C ultraviolet disinfection lamps, and air purifiers to disinfect and disinfect the ground and air respectively. The control module is responsible for the movement and obstacle avoidance of the robot. The navigation module uses Raspberry Pi and LiDAR to achieve real-time robot positioning and two-dimensional plane mapping. In terms of robot software,we have done the following work: (1) Based on the ROS framework, we have implemented functions such as SLAM mapping, location positioning, and odometer data calibration.(2) Customize communication protocols to manage peripheral devices such as UV-C lights, ultrasonic atomizers, air purifiers, and motors on the control board. (3) Develop an Android mobile app that utilizes ROSBridge's lightweight communication architecture to achieve cross platform data exchange between mobile devices and navigation boards, as well as network connectivity and interaction between mobile phones and robots Finally, this thesis implements an engineering prototype of a household disinfection robot from the perspective of product development

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

Propuesta de inclusión de esfuerzos en el control de un brazo robot para asegurar el cumplimiento de la rugosidad superficial durante operaciones de lijado en diferentes materiales

Tesis por compendio[ES] El mecanizado con brazos robots ha sido estudiado aproximadamente desde los años 90, durante este tiempo se han llevado a cabo importantes avances y descubrimientos en cuanto a su campo de aplicación. En general, los robots manipuladores tienen muchos beneficios y ventajas al ser usados en operaciones de mecanizado, tales como, flexibilidad, gran área de trabajo y facilidad de programación, entre otras, frente a las Máquinas Herramientas de Control numérico (MHCN) que necesitan de una gran inversión para trabajar piezas muy grandes o incrementar sus grados de libertad. Como desventajas, frente a las MHCN, los brazos robóticos poseen menor rigidez, lo que combinado con las altas fuerzas producidas en los procesos de mecanizado hace que aparezcan errores de precisión, desviaciones en las trayectorias, vibraciones y, por consiguiente, una mala calidad en las piezas fabricadas. Entre los brazos robots, los brazos colaborativos están en auge debido a su programación intuitiva y a sus medidas de seguridad, que les permiten trabajar en el mismo espacio que los operadores sin que estos corran riesgos. Como desventaja añadida de los robots colaborativos se encuentra la mayor flexibilidad que estos tienen en sus articulaciones, debido a que incluyen reductores del tipo Harmonic drive. El uso de un control de fuerza en procesos de mecanizado con brazos robots permite controlar y corregir en tiempo real las desviaciones generadas por la flexibilidad en las articulaciones del robot. Utilizar este método de control es beneficioso en cualquier brazo robot; sin embargo, el control interno que incluyen los robots colaborativos presenta ventajas que permiten que el control de fuerza pueda ser aplicado de una manera más eficiente. En el presente trabajo se desarrolla una propuesta real para la inclusión del control de esfuerzos en el brazo robot, así como también, se evalúa y cuantifica la capacidad de los robots industriales y colaborativos en tareas de mecanizado. La propuesta plantea cómo mejorar la utilización de un control de fuerza por bucle interior/exterior aplicado en un brazo colaborativo cuando se desconocen los pares reales de los motores del robot, así como otros parámetros internos que los fabricantes no dan a conocer. Este bucle de control interior/exterior ha sido utilizado en aplicaciones de pulido y lijado sobre diferentes materiales. Los resultados indican que el robot colaborativo es factible para realizar tales operaciones de mecanizado. Sus mejores resultados se obtienen cuando se utiliza un bucle de control interno por velocidad y un bucle de control externo de fuerza con algoritmos, Proporcional-Integral-Derivativo o Proporcional más Pre-Alimentación de la Fuerza.[CA] El mecanitzat amb braços robots ha estat estudiat aproximadament des dels anys 90, durant aquest temps s'han dut a terme importants avanços i descobriments en el que fa al seu camp d'aplicació. En general, els robots manipuladors tenen molts beneficis i avantatges al ser usats en operacions de mecanitzat, com ara, flexibilitat, gran àrea de treball i facilitat de programació, entre d'altres, davant de Màquines Eines de Control Numèric (MECN) que necessiten d'una gran inversió per treballar peces molt grans o incrementar els seus graus de llibertat. Com a desavantatges, enfront de les MECN, els braços robòtics posseeixen menor rigidesa, el que combinat amb les altes forces produïdes en els processos de mecanitzat fa que apareguin errors de precisió, desviacions en les trajectòries, vibracions i, per tant, una mala qualitat en les peces fabricades. Entre els braços robots, els braços col·laboratius estan en auge a causa de la seva programació intuïtiva i a les seves mesures de seguretat, que els permeten treballar en el mateix espai que els operadors sense que aquests corrin riscos. Com desavantatge afegida als robots col·laboratius es troba la major flexibilitat que aquests tenen en les seves articulacions, a causa de que inclouen reductors del tipus Harmonic drive. L'ús d'un control de força en processos de mecanitzat amb braços robots permet controlar, i corregir, en temps real les desviacions generades per la flexibilitat en les articulacions del robot. Utilitzar aquest mètode de control és beneficiós en qualsevol braç robot, però, el control intern que inclouen els robots col·laboratius presenta avantatges que permeten que el control de força es puga aplicar d'una manera més eficient. En el present treball es desenvolupa una proposta real per a la inclusió del control d'esforços en el braç robot, així com s'avalua i quantifica la capacitat dels robots industrials i col·laboratius en tasques de mecanitzat. La proposta planteja com millorar la utilització d'un control de força per bucle interior/exterior aplicat en un braç col·laboratiu, quan es desconeixen els parells reals dels motors del robot, així com altres paràmetres interns que els fabricants no donen a conèixer. Aquest bucle de control interior/exterior ha estat utilitzat en aplicacions de polit sobre diferents materials. Els resultats indiquen que el robot col·laboratiu és factible de realitzar aquestes operacions de mecanitzat. Els seus millors resultats s'obtenen quan s'utilitza un bucle de control intern per velocitat i un bucle de control extern de força amb els algoritmes Proporcional-Integral-Derivatiu o Proporcional més Pre-alimentació de la Força.[EN] Machining with robot arms has been studied approximately since the 90s; during this time, important advances and discoveries have been made in its field of application. In general, manipulative robots have many benefits and advantages when they are used in machining operations, such as flexibility, large work area, and ease of programming, among others, compared to Numerical Control Machine Tools (NCMT) that need a great investment to work very large pieces or increase their degrees of freedom. As for disadvantages, compared to NCMT, robotic arms have lower rigidity, which, combined with the high forces produced in machining processes, causes precision errors, path deviations, vibrations, and, consequently, poor quality in the manufactured parts. Among robot arms, collaborative arms are on the rise due to their intuitive programming and safety measures, which allow them to work in the same space without risk for the operators. An added disadvantage of collaborative robots is their flexibility in their joints because they include Harmonic drive type reducers. The use of force control in machining processes with robot arms makes possible to control and correct, in real-time, the deviations generated by the flexibility in the robot's joints. The use of this control method is beneficial for any robot arm. However, the internal control included in collaborative robots has advantages that allow the force control to be applied more efficiently. In this work, a real proposal is developed to include effort control in the robot arm. The capacity of industrial and collaborative robots in machining tasks is evaluated and quantified. The proposal recommends how to improve the use of an inner/outer force control loop applied in a collaborative arm, when the real torques of the robot's motors are unknown and other internal parameters that manufacturers do not disclose. This inner/outer control loop has been used in polishing and sanding applications on different materials. The results indicate that the collaborative robot is feasible to perform such machining operations. Best results are obtained using an internal velocity control loop and external force control loop with Proportional-Integral-Derivative or Proportional plus Feed Forward.The authors are grateful for the financial support of the Spanish Ministry of Economy and European Union, grant DPI2016-81002-R (AEI/FEDER, UE). This work was funded by the CONICYT PFCHA/DOCTORADO BECAS CHILE/2017 – 72180157.Pérez Ubeda, RA. (2022). Propuesta de inclusión de esfuerzos en el control de un brazo robot para asegurar el cumplimiento de la rugosidad superficial durante operaciones de lijado en diferentes materiales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182000TESISCompendi

Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot

One of the technologies in the industrial world that utilizes robots is the delivery of goods in warehouses, especially in the goods distribution process. This is very useful, especially in terms of resource efficiency and reducing human error. The existing system in this process usually uses the line follower concept on the robot's path with a camera sensor to determine the destination location. If the line and destination are not detected by the sensor or camera, the robot's navigation system will experience an error. it can happen if the sensor is dirty or the track is faded. The aim of this research is to develop a robot navigation system for efficient goods delivery in warehouses by integrating odometry and Dijkstra's algorithm for path planning. Holonomic robot is a robot that moves freely without changing direction to produce motion with high mobility. Dijkstra's algorithm is added to the holonomic robot to obtain the fastest trajectory. by calculating the distance of the node that has not been passed from the initial position, if in the calculation the algorithm finds a shorter distance it will be stored as a new route replacing the previously recorded route. the distance traversed by the djikstra algorithm is 780 mm while a distance of 1100 mm obtains the other routes. The time for using the Djikstra method is proven to be 5.3 seconds faster than the track without the Djikstra method with the same speed. Uneven track terrain can result in a shift in the robot's position so that it can affect the travel data. The conclusion is that odometry and Dijkstra's algorithm as a planning system and finding the shortest path are very efficient for warehouse robots to deliver goods than ordinary line followers without Dijkstra, both in terms of distance and travel time

ROS 2 Configuration for Delta Robot Arm Kinematic Motion and Stereo Camera Visualization

The Delta robot is one of the robot types that is used in agriculture and industrial application. However, before the complex physical development of the robot, a simulation needs to be developed to ensure the perfect functionality of the design. Therefore, this paper presented a development of simulation for a parallel delta robot using a Robot Operating System 2 (ROS 2) environment and stereo camera visualization.  The contribution of this research is to present the development details and the proposed solution to solve issues encountered during the development. The development of script in the format of eXtensible Markup Language (XML), Unified Robot Description Format (URDF), and Simulation Description Format (SDF) are presented for describing a robot's physical structure, allowing a robotic system to be depicted in a tree structure, and defining the delta robot arm, which is made up of closed-loop kinematic chain linkage that will be simulated in Gazebo. For the results, several Gazebo plugin libraries are compared and tested for the wheels motion control, stereo camera visualization, and delta robot arm kinematic motion. From the experiment, the best method is inverse kinematic motion the method is selected and used in the simulation. The selected method resulted in an average percentage error of 3.92%, 3.72%, and 2.92%, respectively for each joint

Shared perception is different from individual perception: a new look on context dependency

Human perception is based on unconscious inference, where sensory input integrates with prior information. This phenomenon, known as context dependency, helps in facing the uncertainty of the external world with predictions built upon previous experience. On the other hand, human perceptual processes are inherently shaped by social interactions. However, how the mechanisms of context dependency are affected is to date unknown. If using previous experience - priors - is beneficial in individual settings, it could represent a problem in social scenarios where other agents might not have the same priors, causing a perceptual misalignment on the shared environment. The present study addresses this question. We studied context dependency in an interactive setting with a humanoid robot iCub that acted as a stimuli demonstrator. Participants reproduced the lengths shown by the robot in two conditions: one with iCub behaving socially and another with iCub acting as a mechanical arm. The different behavior of the robot significantly affected the use of prior in perception. Moreover, the social robot positively impacted perceptual performances by enhancing accuracy and reducing participants overall perceptual errors. Finally, the observed phenomenon has been modelled following a Bayesian approach to deepen and explore a new concept of shared perception.Comment: 14 pages, 9 figures, 1 table. IEEE Transactions on Cognitive and Developmental Systems, 202

A Robotic System for In-Situ Measurement of Soil Total Carbon and Nitrogen

Surges in the cost of fertilizer in recent times coupled with the environmental effects of their over-application have driven the need for farmers to optimize the amount of fertilizer they apply on the farm. One of the key steps in determining the right amount of fertilizer to apply in a given field is measuring the amount of nutrients present in the soil. To ascertain nutrient deficiencies, most farmers perform wet chemistry analysis of soil samples which requires a lot of time and is expensive. In this research project, a robotic system was designed and developed that could autonomously move to predetermined GPS waypoints and estimate total carbon (TC) and total nitrogen (TN) content in the soil in-situ using visible and near-infrared reflectance spectroscopy - a faster and cheaper method to determine soil nutrients in real-time. For the locomotion of the robotic system, a Husky robotic platform by Clearpath Robotics was used. A Gen2 robotic arm by Kinova Robotics was used for the precise positioning of the probe in taking soil spectral measurement. The probe was custom designed and built to be used in conjunction with the robotic arm as an end-effector. Two lightweight and inexpensive spectrometers by OceanInsight, namely, Flame VisNIR and Flame NIR+, were used to capture the spectral signatures of soil. The prediction was done with a spectroscopic calibration model and External Parameter Orthogonalization (EPO) was applied to remove the moisture effect from the soil spectra. The robotic system was tested at University of Nebraska-Lincoln (UNL) NU-Spidercam phenotyping facility. Two sets of spectra were obtained from the field campaign: in-situ and dry-ground spectra. The dry-ground spectra were used as library scans and Partial Least Square Regression (PLSR) was used for the modeling. The in-situ spectra were randomly divided into EPO calibration and validation sets. Satisfactory results were obtained from the initial prediction on dry-ground validation set, with R2 (coefficient of determination) of 0.77 and RMSE (Root Mean Squared Error) of 0.15% for TC and R2 of 0.64 and RMSE of 171 ppm for TN. There was a reduction in R2 and an increase in RMSE values for both TC and TN when prediction was done directly on the in-situ validation set. For TC, the R2 dropped and RMSE increased to 0.25 and 0.29% respectively, and for TN, the R2 dropped and RMSE increased to 0.19 and 259 ppm respectively. This was primarily due to the presence of moisture in the field samples. The R2 increased to 0.62 and RMSE decreased to 0.2% for TC, and the R2 increased to 0.51 and RMSE decreased to 200 ppm for TN, when EPO was applied on both the in-situ validation and dry-ground sets. These findings highlight the importance of accounting for moisture effects in the prediction of soil properties using the robotic system and demonstrate the potential of the system in enabling soil monitoring and analysis in-situ. Advisor: Yufeng G

Applications of Intelligent Vision in Low-Cost Mobile Robots

With the development of intelligent information technology, we have entered an era of 5G and AI. Mobile robots embody both of these technologies, and as such play an important role in future developments. However, the development of perception vision in consumer-grade low-cost mobile robots is still in its infancies. With the popularity of edge computing technology in the future, high-performance vision perception algorithms are expected to be deployed on low-power edge computing chips. Within the context of low-cost mobile robotic solutions, a robot intelligent vision system is studied and developed in this thesis. The thesis proposes and designs the overall framework of the higher-level intelligent vision system. The core system includes automatic robot navigation and obstacle object detection. The core algorithm deployments are implemented through a low-power embedded platform. The thesis analyzes and investigates deep learning neural network algorithms for obstacle object detection in intelligent vision systems. By comparing a variety of open source object detection neural networks on high performance hardware platforms, combining the constraints of hardware platform, a suitable neural network algorithm is selected. The thesis combines the characteristics and constraints of the low-power hardware platform to further optimize the selected neural network. It introduces the minimize mean square error (MMSE) and the moving average minmax algorithms in the quantization process to reduce the accuracy loss of the quantized model. The results show that the optimized neural network achieves a 20-fold improvement in inference performance on the RK3399PRO hardware platform compared to the original network. The thesis concludes with the application of the above modules and systems to a higher-level intelligent vision system for a low-cost disinfection robot, and further optimization is done for the hardware platform. The test results show that while achieving the basic service functions, the robot can accurately identify the obstacles ahead and locate and navigate in real time, which greatly enhances the perception function of the low-cost mobile robot