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This study presents a novel 12-degree-of-freedom underactuated wearable device specifically designed for continuous, noninvasive monitoring of spinal posture in nonclinical environments. By integrating a static model with a genetic algorithm for geometric parameter optimization, the device achieves adaptability to multi-DOF spinal motion while minimizing both weight and structural complexity, thus ensuring user comfort. In addition, …

Design, Optimization, and Calibration of a Wearable Spinal Posture Measurement Device Read More »

The purpose of this research is to construct a self-driving bicycle that can balance itself and automatically track a designated trajectory in campus environments. For balancing, a lower-level controller is designed based on the dynamic model of the bicycle. It allows the bicycle to achieve lateral stability and cornering action with robustness to speed variations. …

Balancing and Trajectory Control of a Self-Driving Bicycle Read More »

Based on the Formula Student Driverless (FSD) competition, this thesis is devoted to the construction of a scale-down electric vehicle, and the self-driving system for the vehicle to autonomously run on the track defined by traffic cones. The design of the electric vehicle follows the requirements of the FSD competition. Its propulsion force is provided …

SLAM, Navigation and Torque Vectoring for a Four-Wheel-Drive AWD Autonomous Electric Racing Vehicle Read More »

This research aims to integrate constructive neural networks and virtual robot placement strategy to achieve obstacle avoidance and navigation for multiple robots in a generalized map environment. Deep reinforcement learning theory is applied to design neural networks, which are trained in free space to improve the performance of the dual robot system in obstacle avoidance …

Constructive Extension of Deep Reinforcement Learning Network for Multi-Robot Obstacle Avoidance and Navigation in Generalized Map Environment Read More »

This research proposes a hierarchical control structure combined with reinforcement learning for the stable walking of bipedal robots. The hierarchical control structure consists of a walking trajectory planner and a low-level motion controller. The walking trajectory planner generates a CoM trajectory based on the predefined zero moment point (ZMP) trajectory. The low-level controller consists of …

Reinforcement Learning Optimization of Optimalwalking Walking trajectory Trajectory for a bipedal Bipedal robot Robot under Hybrid Kinematics/Dynamics Control Read More »

在本研究中，我們建立了一人型機器人與等同大小的腳踏車。此人型機器人被設計來踩踏、平衡與駕駛此腳踏車。我們特別專注於設計控制系統使機器人能藉由操控把手來平衡與駕駛腳踏車。我們所提出的控制系統具有估測重心的能力，因之可在未知重心位置下達到平衡的性能。此控制系統是基於一廣用的控制架構，此控制架構能在未知量測偏差的狀況下達到漸近穩定的控制效能。我們也發展了一系統化的程序來設計相關的控制參數。我們以模擬與實驗驗證控制系統能達到重心自適應的性能，特別是在重心不平衡下，使機器人控制腳踏車沿直線行駛。