Search Results (224)

In addressing the challenges of low path tracking accuracy and poor robustness during tractor autonomous operation, this paper proposes a path tracking control method for tractors that integrates prescribed performance with sliding mode control (SMC). A key feature of this control method is its inherent immunity to system parameter perturbations and external disturbances, while ensuring path tracking errors are constrained within a predefined range. First, the tractor is simplified into a two-wheeled vehicle model, and a path tracking error model is established based on the reference operation trajectory. By defining a prescribed performance function, the constrained tracking control problem is transformed into an unconstrained stability control problem, guaranteeing the boundedness of tracking errors. Then, by incorporating SMC theory, a prescribed performance sliding mode path tracking controller is designed to achieve robust path tracking and error constraint for the tractor. Finally, both simulation and field experiments are conducted to validate the method. The results demonstrate that compared with the traditional SMC method, the proposed method effectively mitigates the impact of complex farmland conditions, reducing path tracking errors while enforcing strict error constraints. Field experiment data shows the proposed method achieves an average absolute error of 0.02435 m and a standard deviation of 0.02795 m, confirming its effectiveness and superiority. This research lays a foundation for the intelligent development of agricultural machinery. Full article

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development. Full article

In dogs with intervertebral disc extrusion (IVDE), the Glasgow Composite Measure Pain Scale—Short Form (GCMPS) and the Sharp and Wheeler Grading Scale (SWGS) are routinely used in the evaluation of pain (GCMPS) and neurological function (SWGS). Additionally, quantitative sensory tests (QSTs) are increasingly being incorporated into veterinary clinical practice for pain characterisation. The aim was to investigate a possible relationship between the GCMPS, the SWGS, and mechanical thresholds (MTs) in 31 client-owned dogs with thoracolumbar IVDEs. Dogs were always assessed in the same order, starting with pain rating using the GCMPS, followed by classifying neurological severity using the SWGS, before determining MTs using a handheld pressure algometer. Dogs were evaluated over a five-day testing period (before surgery and on days one, two, three, and ten after surgery). The GCMPS and the SWGS data remained consistent across all days of testing. No statistically significant correlation or difference was observed between the scores. MTs showed a significant negative correlation with the GCMPS (r = −0.311; p < 0.001) and a positive one with the SWGS (r = 0.282; p = 0.002). The GCMPS and MTs showed a slight divergence in their progression. MTs might be more sensitive than GCMPS in reflecting clinical improvement and should be considered for clinical practice. Full article

Our study explored the accuracy of Fitbit recorded step count during the use of an assistive device (two-wheeled walker and standard cane) at various body positions (wrists, hips, and ankles). Participants (n = 11) ambulated an obstacle course (50 m total, including turns and a step up/down) a total of three times (two-wheeled walker, standard cane, and a deviceless control trial). Fitbit generated step counts (at the wrists, hips, and ankles) were then compared to the individual’s “actual” step count captured through video analysis. During the cane trial and the deviceless trial, all positions were significantly correlated (r = 0.764 to 0.984; p < 0.006) with the actual step count. However, increased variability (demonstrated by increased limits of agreement) was observed when the Fitbit was worn on the wrist (compared to the hips or ankles). During the walker trial, the step count was significantly correlated to the actual step count at the ankle and hip positions (r = 0.669 to 0.888; p < 0.017) with an average error of 1.5%, while it was not statistically correlated at the wrist with a 31.2% average error. Our study suggests that Fitbits are a good predictor of actual step count, with the caveat that the location of the Fitbit should be considered if an assistive device (e.g., two-wheeled rolling walker and single-point cane) is being used. Full article

This paper proposes a method for solving mazes, with a special focus on navigation using image processing. The objective of this study is to demonstrate that a robot can successfully navigate a maze using only two-wheel encoders, enabled by appropriate control strategies. This method significantly simplifies the structure of mobile robots, which typically suffer from increased energy consumption due to the need to carry onboard sensors and power supplies. Through experimental analysis, it was observed that although the encoder-only solution requires more advanced control knowledge, it can be more efficient than the alternative approach that combines encoders with a gyroscope. In order to develop an efficient maze-solving system, control theory techniques were integrated with image processing and neural networks in order to analyze images in which various obstacles were transformed into maze walls. This approach led to the training of a neural network designed to detect key points within the maze. Full article

A modified dual hierarchical terminal sliding mode control (MDHTSMC) strategy is developed in this study for the control of a two-wheeled self-balancing robot (TWSBR). The control framework incorporates individually designed sliding surfaces within a structured dual-layer hierarchy, enabling explicit prediction of convergence time. To overcome the system’s underactuation characteristics, a hierarchical structure is embedded into the dual terminal sliding mode control law. Additionally, the proposed approach mitigates the chattering effect and enhances the system’s self-balancing capabilities. Numerical simulations were conducted to verify the controller’s effectiveness and to confirm the theoretical results. Full article

Sustainability and resource optimization have spurred interest in giving a second life to used equipment, often discarded after limited use. Within this framework, we conducted a multidisciplinary, final-year engineering project to explore the reverse engineering and repurposing of commercial hoverboards for an auto-stabilizing, modular robotic platform, with emphasis on medical applications such as transporting medication. The innovation lies in recycling hoverboards to develop a teleoperated, stabilized base that can accommodate additional modules—for instance, a multifunctional arm or a transport shelf—akin to existing commercial robots. Our methodology involves disassembling and reprogramming the hoverboard’s motor controllers and sensors to maintain horizontal stability. Control is realized through the sensor fusion of accelerometer and gyroscope data, processed by a Kalman filter and implemented in a Proportional-Integral-Derivative (PID) loop. A user-friendly Human-Machine Interface (HMI), hosted on an ESP32 microcontroller, enables remote operation and monitoring. Experimental results show that the platform autonomously balances, carries payloads, and achieves high energy efficiency, highlighting its potential as a sustainable and versatile solution in modular robotic applications. Full article

Motorcycle horns are a dominant source of urban noise in many Southeast Asian cities, driven by high two-wheeler density and limited public transport infrastructure. Although automobiles have been in use for over a century, regulations governing horn design and volume control remain inadequate. This study investigates horn use behavior in Vietnamese urban traffic, identifying distinct acoustic patterns categorized as “attention” and “warning” signals. Measurements conducted in an anechoic chamber reveal that these patterns can increase sound pressure levels by up to 17 dB compared to standard horn use, with notable differences in frequency components. These levels often exceed the daytime noise thresholds recommended by the World Health Organization (WHO), indicating potential risks for adverse health outcomes, such as elevated stress, hearing damage, sleep disturbance, and cardiovascular effects. The findings are contextualized within broader efforts to manage traffic noise in rapidly developing urban areas. Drawing parallels with studies on aircraft noise exposure in Japan, this study suggests that long-term exposure, rather than peak noise levels alone, plays a critical role in shaping community sensitivity. The study results support the need for updated noise regulations that address both the acoustic and perceptual dimensions of road traffic noise. Full article

Road traffic emissions are still a significant contributor to air pollution, which causes adverse health effects. Remote emission sensing (RES) is a state-of-the-art technique that continuously monitors the emissions of thousands of vehicles in traffic. Automatic number plate recognition (ANPR) systems are an essential part of RES systems to identify the registered owners of high-emitting vehicles. Recognizing number plates on L-vehicles (two-wheelers) with a standard ANPR system is challenging due to differences in size and placement across various categories. No ANPR system is designed explicitly for Category L vehicles, especially mopeds. In this work, we present an automatic number plate detection and recognition system for Category L vehicles (L-ANPR) specially developed to recognize L-vehicle number plates of various sizes and colors from different categories and countries. The cost-effective and energy efficient L-ANPR system was implemented on roads during remote emission measurement campaigns in multiple European cities and tested with hundreds of vehicles. The L-ANPR system recognizes Category L vehicles by calculating the size of each passing vehicle using photoelectric sensors. It can then trigger the L-ANPR detection system, which begins detecting license plates and recognizing license plate numbers with the L-ANPR recognizing system. The L-ANPR system’s license plate detection model is trained using thousands of images of license plates from various types of Category L vehicles across different countries, and the overall detection accuracy with test images exceeded 90%. The L-ANPR system’s character recognition is designed to identify large characters on standard number plates as well as smaller characters in various colors on small, moped license plates, achieving a recognition accuracy surpassing 70%. The reasons for false recognitions are identified and the solutions are discussed in detail. Full article

Two new species of the genus Oxyopomyrmex are described from Spain (Iberian Peninsula). The first belongs to the magnus group—characterized by a head more wide than long—and is distinguished by strongly developed cephalic striations, pale coloration (light brown) and a relatively small body size. To date, it has been recorded in the provinces of Jaén and Granada. The second species is even smaller in size, with very faint cephalic striations and short, triangular propodeal spines—morphological features that clearly set it apart from all previously known species. Its current distribution appears to be restricted to the province of Huelva, near the Atlantic coast, including the iconic Doñana National Park. With these additions, the number of Oxyopomyrmex species known from Spain increases from two to four. To date, sampling efforts have only focused on the southern part of the country, suggesting that additional, undiscovered species may still exist. Full article

Navigation is a fundamental technology for mobile robots. However, many trajectory planning methods suffer from curvature discontinuities, leading to instability during robot operation. To address this challenge, this paper proposes a navigation scheme that adheres to the kinematic constraints of a two-wheeled differential-drive robot. An improved and efficient RRT algorithm is employed for global navigation, while an adaptive clothoid curve is utilized for local trajectory smoothing. Simulation results demonstrate that the proposed method effectively eliminates curvature discontinuities and enhances operational efficiency. Full article

This paper presents a control framework for the image tracking of two-wheeled self-balancing carts, with the objective of achieving precise tracking control. Exploiting the remarkable memory capacity of the Long Short-Term Memory (LSTM) neural network for sequence signals, the framework conducts image memory judgment and memorization, aiming to enhance control accuracy. After the training phase, comprehensive simulations and real-world experiments are carried out based on the established model to verify the effectiveness and practicality of the proposed control strategy. The system utilizes the TSL1401 linear array CCD lens to detect black tapes on the ground and identify and memorize surrounding images. Through the establishment of a continuous set of training sample points, the LSTM network is trained using Python and TensorFlow. This training process optimizes the network’s weights and generates weight files, which can be readily converted into machine code for physical implementation. Initially, the effectiveness of the control law is verified through simulating the symmetrical steering control of the two-wheeled cart. The simulation results demonstrate the validity of the proposed design method and its superior performance. Finally, a physical two-wheeled self-balancing cart is developed to further validate the feasibility of the framework. Experimental results confirm that this method is highly effective, demonstrating robust image tracking capabilities and optimal tracking performance. Full article

This paper proposes a fuzzy PDC (parallel distributed compensation)-based LQR (Linear Quadratic Regulator) sliding neural network methodology to control a two-wheeled self-balancing cart. Firstly, a mathematical model of a two-wheeled self-balancing cart is described to explain some parameter meanings. Then, we detail how a simulation was implemented according to these reasonable parameter settings under the fuzzy PDC-based LQR sliding neural network control algorithm. Secondly, the algorithm is developed by setting four controllable LQR controllers. Then, a ReLU-based neural network (ReNN) is developed to tune the fuzzy degrees for these four LQR controllers. This means that an intelligent controller is designed by using the fuzzy PDC concept. Subsequently, a sliding surface is designed, and the sliding mode is utilized to compensate and enhance its stability. Simulation was conducted to verify the feasibility of this proposed algorithm. The simulation results demonstrate good effectiveness and stability. Finally, a cart equipped with an STM32 MCU (microcontroller unit) was implemented to verify the feasibility of this proposed algorithm. The empirical experimental results show that the two-wheeled self-balancing cart exhibited good self-balancing performance and stability. Full article

Background/Objectives: The present study aimed to investigate letter processing in children with dyslexia and typically developing readers as a function of the type of orthographic context. Methods and Results: In Experiment 1A, children performed a two-alternative forced choice task (Reicher–Wheeler paradigm) using as probes either high-frequency words, pronounceable pseudo-words, or unpronounceable non-words. The group differences in letter recognition were clearly distinguished from those present in typical word and pseudo-word reading conditions (Experiment 1B), as a global factor was present only in the latter case. In Experiment 2, the two-alternative forced choice task required the child to search for the target letter in the subsequent multi-letter string (i.e., words, pseudo-words, or non-words), thus reducing the memory load. Detecting the target letter was more difficult in a word than in a pseudo-word or non-word array, indicating that the word form’s lexical activation interfered with the target’s analysis in both groups of children. In Experiment 3, children performed the two-alternative forced choice task with symbols (Greek letters) either in the Reicher–Wheeler mode of presentation (Experiment 3A) or in the search condition (Experiment 3B). Children with dyslexia performed identically to typically developing readers in keeping with the selectivity of their orthographic difficulties. Conclusions: The present data indicate that children with dyslexia suffer from an early deficit in making perceptual operations that require the conjunction analysis of a set of letters. Still, this deficit is not due to an inability to scan the letter string. The deficit is confined to orthographic stimuli and does not extend to other types of visual targets. Full article

A STEM education provides students with a friendly and efficient environment for learning science, technology, engineering, and math. According to the needs of STEM programs and activities, humanoid, biped, and quadruped robots have been developed. Those robots are used as a learning tool supporting students in exploring the principles and theory of robotics and their related applications. In addition, those robots adapt open sources to provide free instructions for the students to build their own low-cost robots. To enhance the effects, a low-cost, two-wheel robot was created in this study. Unlike other robots, two-wheel robots usually require a gyroscope sensor and a motion controller to keep them balanced. The developed robot is an integrated system including hardware and software. Its hardware consists of an ESP32 microcontroller, a pair of DC motors, a gyroscope sensor MPU6050, and a driver for DC motors. The robot receives signals “angle” from the gyroscope, and then depends on the PID approach to drive the DC motors precisely in order to achieve balanced and smooth motions. The results of this study present the design of the robot, sensor calibration methods, and proportional-integral-derivative tuning. Full article