Mobile Robot Platform Research Articles

How Brains Perceive the World.

Can machines ever be sentient? Could they perceive and feel things, be conscious of their surroundings? What are the prospects of achieving sentience in a machine? What are the dangers associated with such an endeavor, and is it even ethical to embark on such a path to begin with? In the series of articles of this column, I discuss one possible path toward "general intelligence" in machines: to use the process of Darwinian evolution to produce artificial brains that can be grafted onto mobile robotic platforms, with the goal of achieving fully embodied sentient machines.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Rapid distance estimation of odor sources by electronic nose with multi-sensor fusion based on spiking neural network

The inference of odor source locations holds great potential value in chemical leak detection, explosive searches and flammable substance detection. Sensor response and derivative changes are common indicators used to estimate source distance. However, most studies often rely on single-feature methods using a single sensor. In this study, a novel distance indicator based on a spiking neural network (SNN) that integrates multiple sensors is proposed. SNN was introduced for effective multi-sensor data fusion, which can provide accurate and rapid source distance estimation. The indicator's accuracy and quick detection capabilities were tested using a publicly available wind tunnel dataset and compared with single-sensor indicators. Additionally, an olfactory robot platform was constructed to test the method's real-time performance. Results demonstrate that this method effectively utilizes sensor arrays to estimate source distance, with an average root mean square error (RMSE) of less than 0.1 m across different wind speeds, outperforming single-sensor-based indicators. Additionally, the model exhibits good distance estimation performance within a relatively short detection time, with an average RMSE of 0.118 m. Practical testing on a mobile robot platform confirms the applicability of this distance indicator for real-time distance monitoring. In conclusion, the proposed distance indicator enables effective distance estimation for odor sources, providing a promising method for odor source localization and olfactory navigation.

Multi-Robot Collaborative Mapping with Integrated Point-Line Features for Visual SLAM.

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.

A single-layer compton camera for nuclear decommissioning: An improved back-projection algorithm

The decommissioning of nuclear facilities, a critical and hazardous process due to radiation exposure, necessitates the advancement of radiological measurement techniques concerning safety and efficiency. This study presents an optimized back-projection method, integrating a novel single-layer Compton camera and 3D camera setup with the Timepix3 readout chip to improve the precision and efficiency of gamma-ray source localization in decommissioning scenarios. Implementing ‘twin addition’ and ‘twin multiplication’ techniques addressed data ambiguities that arise from event selection in the Compton camera, enhancing the reliability of localization. Introducing a zoom function significantly improved computational efficiency, achieving 163 times faster computation times without sacrificing accuracy. Our method demonstrated enhanced precision with a median angular error of 1.41°, outperforming traditional methods and showing competitive advantages over state-of-the-art technologies, including the Caliste-HD detector. The feasibility of integrating this methodology onto mobile robotic platforms suggests a promising avenue to minimize human radiation exposure and optimize decommissioning tasks, ensuring safer and more effective nuclear facility decommissioning.

A Passive Power-Based Control Strategy for pHRI Tasks With Omni-Directional Robotic Mobile Platforms

A Passive Power-Based Control Strategy for pHRI Tasks With Omni-Directional Robotic Mobile Platforms

BinVPR: Binary Neural Networks towards Real-Valued for Visual Place Recognition.

Visual Place Recognition (VPR) aims to determine whether a robot or visual navigation system locates in a previously visited place using visual information. It is an essential technology and challenging problem in computer vision and robotic communities. Recently, numerous works have demonstrated that the performance of Convolutional Neural Network (CNN)-based VPR is superior to that of traditional methods. However, with a huge number of parameters, large memory storage is necessary for these CNN models. It is a great challenge for mobile robot platforms equipped with limited resources. Fortunately, Binary Neural Networks (BNNs) can reduce memory consumption by converting weights and activation values from 32-bit into 1-bit. But current BNNs always suffer from gradients vanishing and a marked drop in accuracy. Therefore, this work proposed a BinVPR model to handle this issue. The solution is twofold. Firstly, a feature restoration strategy was explored to add features into the latter convolutional layers to further solve the gradient-vanishing problem during the training process. Moreover, we identified two principles to address gradient vanishing: restoring basic features and restoring basic features from higher to lower layers. Secondly, considering the marked drop in accuracy results from gradient mismatch during backpropagation, this work optimized the combination of binarized activation and binarized weight functions in the Larq framework, and the best combination was obtained. The performance of BinVPR was validated on public datasets. The experimental results show that it outperforms state-of-the-art BNN-based approaches and full-precision networks of AlexNet and ResNet in terms of both recognition accuracy and model size. It is worth mentioning that BinVPR achieves the same accuracy with only 1% and 4.6% model sizes of AlexNet and ResNet.

Mechanical Survey on Wheeled Mobile Robot Platform for Industrial and Personal Service Robots

Mechanical Survey on Wheeled Mobile Robot Platform for Industrial and Personal Service Robots

Dynamic Output Feedback of Second-Order Systems: An Observer-Based Controller with Linear Matrix Inequality Design

This paper presents an observer-based dynamic output-feedback controller design procedure using linear matrix inequality (LMI) optimization for second-order systems with uncertainty and persistent perturbation in the states. Using linear-quadratic criteria, cost functions are minimized in a two-stage procedure to compute optimal state-feedback gains, and observer gains are coupled into a dynamic output-feedback optimal controller. The LMI set used in the two stages is matrix inversion free, a key issue for polytope formulation when uncertainty is present. The approach is tested in a mobile inverted pendulum robotic platform, and the effectiveness is verified in this underactuated and undesensed case.

Design of a Mobile Industrial Robot Platform based on Fixed Cameras

This study presents a design for an autonomous mobile robot system that utilizes environmental cameras for navigation and obstacle avoidance. Operating within a known environment, the robot employs these cameras to facilitate path planning and to circumvent obstacles. The hardware setup includes fixed cameras strategically placed in the environment to pinpoint the robot’s location and to identify obstacles amidst varying conditions. A computer system is essential for processing the data captured by the cameras, and a wireless transmission system communicates operational instructions to the robot.For navigation, the study explores various algorithms for path planning, selecting the most efficient one through comparative analysis. Once the path is established, the robot’s instructions are computed under the designed control rate, employing the vehicle’s PD control method. This method is informed by the external camera’s simulation of the vehicle, ensuring accurate and responsive navigation.The project’s goal is to create a mobile robot platform that leverages an external camera as its primary sensor. This design promises efficient autonomous navigation and the capability to avoid static obstacles. The robot’s design is conceptualized using SolidWorks and brought to life through implementation in the Python and MATLAB programming languages, demonstrating the system’s practicality and adaptability in real-world scenarios. Additionally, the system’s design considers ease of integration and scalability to accommodate future technological advancements and new application requirements.

Development of an IoT based 3D printed Mobile Robot Platform for training of Mechatronics Engineering Students

The advent of the Internet of Things (IoT) has opened up new opportunities in education, particularly in the field of mobile robotics. Mobile robotics is a fast-growing field, and with the increasing demand for skilled robotics engineers, it is essential to provide students with hands-on experience in designing and developing robots. In this regard, an IoT-based 3D printed mobile robot platform has been developed for students training. STEM (Science, Technology, Engineering, and Mathematics) education has been gaining attention as a means to improve science and engineering education. STEM education aims to foster critical thinking, problem-solving skills, creativity, and innovation among students. It involves an educational policy that aims to develop the next generation of skilled professionals by integrating the fields of Science, Technology, Engineering and Mathematics. The goal of this approach is to provide students in mechatronics, who are assigned to the robotics lab during their studies, an up to date knowledge and experience in research and development activities. Thus, this paper aims to provide education that focuses on the acquisition and application of engineering knowledge, including control engineering, mechanical engineering, electrical and electronic engineering, information engineering, among others, with the main focus on mobile robot research.

Enhancing accuracy in field mobile robot state estimation with GNSS and encoders

The enhancement of accuracy remains a significant concern in state estimation for field mobile robots. This paper introduces a novel sensor fusion method to refine the accuracy of state estimation, employing global navigation satellite systems (GNSS) and encoders. This technique integrates a nonlinear observer for estimating the state of nonlinear systems and an unscented Kalman filter (UKF) to ascertain the robot’s state variables using signals from GNSS and encoder-based position sensors. In the proposed method, the nonlinear observer updates sigma points through nonlinear transformation and the UKF optimizes state estimation. Parameters are optimized through the particle swarm optimization method with the objective of minimizing the root mean square error of the state. To demonstrate performance, numerical simulations compare the proposed method with well-known nonlinear estimation algorithms, illustrating its effectiveness in field mobile robot state estimation. The method is further validated through implementation on a real-world mobile robot platform.

Design of Interface Board Based on ESP32 Module for Autonomous Mobile Robot Platform

Design of Interface Board Based on ESP32 Module for Autonomous Mobile Robot Platform

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection.

In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model's robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model's parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms.

Control of mobil robots in fuzzy environment

In this work, the application of fuzzy hardware control to the dynamic movement of an autonomous two-wheeled mobile robot under uncertainty is considered the most relevant topic of the day. There are also adverse reactions. These led to an imprecise mathematical model for the two-wheeled robotic test rig. Therefore, the goal of developing a control system for a two-wheeled robot with a fuzzy logic control algorithm was set. The development of fuzzy logic control systems according to modern applications is very important to create intelligent robots that can be used in wider applications. In this work, a fuzzy control model for a non-linear two-wheeled robot system is designed, followed by a real-time implementation of the developed control in a robot device. Finally, the experimental results of the system were analyzed and discussed, and the closed-loop response of the fuzzy logic two-wheeled robot system with PID control was tested. The fuzzy control has a better response than the PID controller. A similar effect was observed for the normalized speed of the motors, as less energy was required to balance and stabilize the two-wheeled robot with fuzzy control. One of the main advantages of the method proposed in the article is to enable the autonomous two-wheeled mobile robot to operate without collision with obstacles and with high dynamic accuracy in a low-obstacle environment. As a result of the simulation, the effectiveness and reliability of the change avoidance behavior in uncertain environments and the speed control of the two-wheeled mobile robot platform of the proposed fuzzy control strategy are realized.

Разработка автоматизированной системы управления мобильным роботом

In modern conditions, when automation is becoming universal, there are many different names and manufacturers of automated control systems on the market. These systems include various devices such as communication facilities, multifunctional signal input/output boards, a variety of controllers, intelligent and non-intelligent sensors, actuators, single-board computers for industrial use and other devices. There are many companies and names in this field offering their products and solutions. Companies such as Siemens, Schneider Electric, ABB, Rockwell Automation, Honeywell and many others. The automated control system of the mobile robot allows for manual and automatic control under the direct supervision of a qualified employee. The main idea in the development of an automatic control system for mobile robots is that the robot moves through the warehouse premises of the enterprise without human intervention, transporting loads of different sizes and choosing the optimal trajectory of movement. The main advantage of mobile robotic platforms is their versatility. Mobile platforms can be used to perform a variety of tasks, such as transporting goods in a confined space or mapping an area. Depending on the characteristics of each mobile platform, you can create control systems to perform a specific task.

A study of the accuracy of industrial robots and laser-tracking for timber machining across the workspace

ABSTRACT The aim of the present study was to investigate the accuracy of an industrial robot when milling glued-laminated timber along a vertical work plane while maximising the advantageous size of the workspace. The authors discovered a research gap in the machining accuracy within the workspace of an industrial robot. Hence, the examination comprises 50 distinct positions allocated in a grid, each with 3 machined specimens resulting in a total of 150 samples. The workpiece positions were referenced using laser-tracking technology to adjust the robot machining code to the actual workpiece position, while simultaneously assessing the machining accuracy using laser scans of the samples. As a result, accuracy maps are presented indicating the nominal-actual deviation including referencing errors and workpiece dimensional deviations (absolute accuracy) and the robot-only nominal-actual deviation (relative accuracy), subsequently proving that both comply with the EN 14080 standard. Furthermore, a calculation of the centre of accuracy defines the point of highest relative accuracy. The objective was achieved and advocated for a more pronounced utilisation of the workspace when machining timber with industrial robots. Applying these insights to the machining of large complex parts, multi-workpiece stacks and mobile robot platforms can enhance the possibilities and efficiency within timber construction.

Passive Visual Odometry of Mobile Transport Robots in Production Areas Using Anchor Points

The article considers the problem of determining the orientation of mobile transport robots, which are now actively used in almost all areas of industry. They make it possible to intensify production, free personnel from performing routine operations, and exclude the influence of the human factor from work. Based on the analysis of various variants of active orientation methods involving the use of binocular video systems installed on the vehicles themselves, it is proposed to use a passive orientation detection system. It includes a single stationary monocular video system, the replacement of onboard video systems on mobile robots with simple radiation sensors, as well as the use of anchor points in the area of their movement. The order of choice of anchor points in solving the problem of orientation of a mobile transport robot using a monocular video system, in particular for a rectangular interior space, is considered. Calculation formulas for determining the coordinates of the sensor from the pixel coordinates of its image obtained from a monocular video camera are determined. The general sequence of actions for determining the orientation of a mobile robotic platform is also considered. Unlike active systems, this method makes it possible to significantly simplify the hardware, significantly facilitate the analysis of the current position of the mobile robot and thereby reduce the computational complexity of calculations due to the fact that there is no need to use a complex mathematical apparatus. It is being replaced by simpler two-dimensional geometric calculations. This approach, due to the unified management, makes it possible to effectively coordinate the actions of a group of mobile transport robots when they are used together, greatly simplifies the solution of a number of tasks for optimizing the intra-shop movement of mobile vehicles.

IOT Based All-in-One Robot (Agent-Eye)

Agent-Eye is a versatile project that involves creating a robotic system for surveillance or reconnaissance purposes. The key components typically include a mobile robot platform, camera(s), sensors for detecting mines in military, and a communication module It uses commands to move car in front, back and left right directions. Whatever is recorded by the camera can be viewed in Mobile/PC for reference. There is a metal detector sensor is used for detecting bomb/mines in war. The servo motor is used to rotate wheels of robot car. Using Arduino we implement the logic of move car in directions like forward, backward, right and left

Design and Development of Automated Control of Restaurant Robot

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.