Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
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....
To mitigate the detrimental effects of joint elasticity and transmission errors on contour accuracy and to improve the multi-axis motion performance of hybrid robots, this study investigates contour error modeling and control by leveraging additional grating sensors for real-time measurements. Accounting for the inherent pose coupling characteristics of hybrid robots, a novel contour error modeling method is proposed that employs six-dimensional exponential coordinates for error description and incorporates an efficient search algorithm for foot point determination. Building upon an existing grating sensor feedback control framework, a proportional contour controller is developed. Experimental validation on the TriMule-200 hybrid robot demonstrates an enhancement in end-effector contour accuracy....
In response to the demand for high-precision positioning within confined or indoor environments, the application of acoustic ranging methods has been widely adopted by numerous engineers. Currently, time-of-flight (TOF)-based acoustic ranging positioning systems face challenges such as the susceptibility of sound velocity to environmental factors and the loss of acoustic signals at both short and long distances, which leads to a reduction in positioning accuracy. This paper addresses these issues by proposing a high-precision confidence interval weighting method for acoustic ranging and further introduces a method for base station deployment and self-optimization positioning within fixed indoor base station scenarios. The method is based on trilateration positioning, establishing criteria for the division of central and boundary areas. It categorizes mobile terminal nodes based on their coordinates from the previous moment, selects distance information from nearby base stations in different modes, and employs weights for decision-making and computation, ultimately yielding two-dimensional positioning coordinates. Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms....
This paper presents a learning-assisted approach for state estimation of quadruped robots using observations of proprioceptive sensors, including multiple inertial measurement units (IMUs). Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing the dynamics of the body and legs, in addition to joint encoders. The extended Kalman filter (KF) is employed to fuse sensor data to estimate the robot’s states in the world frame and enhance the convergence of the extended KF (EKF). To circumvent the requirements for the measurements from the motion capture (mocap) system or other vision systems, the right-invariant EKF (RI-EKF) is extended to employ the foot IMU measurements for enhanced state estimation, and a learning-based approach is presented to estimate the vision system measurements for the EKF. One-dimensional convolutional neural networks (CNN) are leveraged to estimate required measurements using only the available proprioception data. Experiments on real data from a quadruped robot demonstrate that proprioception can be sufficient for state estimation. The proposed learning-assisted approach, which does not rely on data from vision systems, achieves competitive accuracy compared to EKF using mocap measurements and lower estimation errors than RI-EKF using multi-IMU measurements....
The light-sensitive explosive (silver acetylide–silver nitrate, SASN) sprayed on structural surfaces can be synchronously initiated by intense pulsed flash, thereby simulating cold X-ray blow-off events characterized by thermal–mechanical coupling effects. By adjusting the areal density of SASN coatings, proportional blow-off impulse levels can be achieved. To address the challenge of in situ and non-destructive areal density measurement for SASN coatings, this study developed an X-ray fluorescence (XRF) detection system integrated with a six-axis spray robot. Excitation parameters (50 kV, 20 μA) and geometric configuration (6 cm focal distance) were optimized to establish a quadratic calibration model between Ag Kα counts and areal density (0–80 mg/cm2) with high correlation (R2 = 0.9987). Validation experiments were conducted on a uniformly coated SASN plate (20 × 20 cm) to evaluate the consistency between XRF and sampling methods. The XRF-measured areal density averaged 12.722 mg/cm2 with a coefficient of variation (CV) of 3.19%. The reference value obtained by the sampling method was 12.718 mg/cm2 (CV = 1.57%). The relative deviation between the two methods was only 0.03%, confirming the feasibility of XRF for the quantification of SASN coatings. The XRF system completed measurements in 1 h, achieving a 77.8% time reduction compared to conventional sampling (4.5 h), significantly enhancing efficiency. This work provides a reliable solution for in situ and non-destructive quality control of energetic material coatings....
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