This article introduces a self-balancing bicycle utilizing a reaction wheel for stability control. To manage hardware complexity, a simplified test platform was first used for physical modeling and controller design with a linear-quadratic regulator (LQR), validating the concept. For the final implementation, the full-scale bicycle adopts full-state feedback. Although the LQR improved stability, its frequent motor speed adjustments led to high power consumption. To address this, a novel adaptive equilibrium point gradual algorithm (AEPGA) was developed to adapt to environmental changes, such as lateral imbalances. Performance tests on a real bicycle showed the AEPGA achieved maximum speed of 17 km/h on campus roads. In tests with a 2 kg lateral payload, AEPGA achieved a 67.456% reduction in power consumption compared to LQR over 22 s. For path tracking, a 3D LiDAR system was integrated. On a 77 m indoor corridor, the system reached an average speed of 1.39 km/h with a 0.153 m mean absolute error, while on a 190 m outdoor asphalt road, it reached 2.31 km/h with 0.283 m MAE. These results demonstrate the effectiveness of AEPGA in enhancing energy efficiency and adaptability, while maintaining reliable path tracking in varied environments.

This paper presents the My Eyes (MyE) system, a novel approach to address challenges encountered in conventional binocular stereo vision (BSV) systems, particularly pertaining to parameter optimization and region of interest (ROI) fixation, which often lead to suboptimal localization accuracy in certain applications. The MyE system dynamically adjusts the orientation of the binocular camera, similarly to human beings’ eyes, to optimize its positioning for enhanced accuracy, achieved through modifications to parameters, positioning formulas, and error analysis of traditional BSV systems. It addresses mechanism errors and compensates for system structural parameters (SSPs) by defining error parameters and assembly error offsets and providing an error analysis model to elucidate their impact on positioning errors. Additionally, by compensating for SSPs and refining positioning formulas, the proposed system achieves precise localization from varying angles. Extensive experimental validation shows the efficiency of MyE system, with comparison experiments and accuracy evaluations demonstrating its effectiveness in improving BSV system performance, offering valuable insights for its application in diverse fields such as applied optics research and practical applications.

A unique motor–syringe air control system is introduced to power a PneuNet-inspired silicone soft robotic hand. The system consists of a novel motor–syringe air drive (MSAD) pneumatic device with a crank–slider mechanism that integrates the functionalities of common medical syringes and servo motors. This novel system is integrated with pressure and flex sensors to overcome challenges encountered with using traditional compressor-powered actuation systems to achieve superior linear pressure profiles, provide precise control over soft finger movements, and minimize the noise emitted. Our soft finger design is inspired by the architecture of PneuNet pneumatic actuators and is further optimized by performing ANSYS Workbench (version 2023) simulations to considerably enhance the bending efficiency. A pressure sensor is deployed in each finger chamber for the purpose of grasping force control in terms of air pressure. Furthermore, the deployed pressure sensor in each finger chamber can also continuously monitor the air leakage, and a replenishment valve can be activated when the air leakage significantly affects (i.e., less than 95% of target pressure) the actuation to restore atmospheric pressure in the syringe chamber to restore the MSAD function. Different tests on bending and grasping (including grasping objects with various shapes) are performed to verify the performance of the proposed pneumatic actuator and the five soft fingers.