arXiv subjects

Muhtadin

Publications and source records attributed to Muhtadin.

4 recordsLinked to original sources

arXiv2025

Wireless Center of Pressure Feedback System for Humanoid Robot Balance Control using ESP32-C3

Maintaining stability during the single-support phase is a fundamental challenge in humanoid robotics, particularly in dance robots that require complex maneuvers and high mechanical freedom. Traditional tethered sensor configurations often restrict joint movement and introduce mechanical noises. This study proposes a wireless embedded balance system designed to maintain stability on uneven surfaces. The system utilizes a custom-designed foot unit integrated with four load cells and an ESP32-C3 microcontroller to estimate the Center of Pressure (CoP) in real time. The CoP data were transmitted wirelessly to the main controller to minimize the wiring complexity of the 29-DoF VI-ROSE humanoid robot. A PID control strategy is implemented to adjust the torso, hip, and ankle roll joints based on CoP feedback. Experimental characterization demonstrated high sensor precision with an average measurement error of 14.8 g. Furthermore, the proposed control system achieved a 100% success rate in maintaining balance during single-leg lifting tasks at a 3-degree inclination with optimized PID parameters (Kp=0.10, Kd=0.005). These results validate the efficacy of wireless CoP feedback in enhancing the postural stability of humanoid robots, without compromising their mechanical flexibility.

cs.RO↗

arXiv2025

Quadrupped-Legged Robot Movement Plan Generation using Large Language Model

Traditional control interfaces for quadruped robots often impose a high barrier to entry, requiring specialized technical knowledge for effective operation. To address this, this paper presents a novel control framework that integrates Large Language Models (LLMs) to enable intuitive, natural language-based navigation. We propose a distributed architecture where high-level instruction processing is offloaded to an external server to overcome the onboard computational constraints of the DeepRobotics Jueying Lite 3 platform. The system grounds LLM-generated plans into executable ROS navigation commands using real-time sensor fusion (LiDAR, IMU, and Odometry). Experimental validation was conducted in a structured indoor environment across four distinct scenarios, ranging from single-room tasks to complex cross-zone navigation. The results demonstrate the system's robustness, achieving an aggregate success rate of over 90\% across all scenarios, validating the feasibility of offloaded LLM-based planning for autonomous quadruped deployment in real-world settings.

cs.RO↗

arXiv2025

Autonomous Grasping On Quadruped Robot With Task Level Interaction

Quadruped robots are increasingly used in various applications due to their high mobility and ability to operate in diverse terrains. However, most available quadruped robots are primarily focused on mobility without object manipulation capabilities. Equipping a quadruped robot with a robotic arm and gripper introduces a challenge in manual control, especially in remote scenarios that require complex commands. This research aims to develop an autonomous grasping system on a quadruped robot using a task-level interaction approach. The system includes hardware integration of a robotic arm and gripper onto the quadruped robot's body, a layered control system designed using ROS, and a web-based interface for human-robot interaction. The robot is capable of autonomously performing tasks such as navigation, object detection, and grasping using GraspNet. Testing was conducted through real-world scenarios to evaluate navigation, object selection and grasping, and user experience. The results show that the robot can perform tasks accurately and consistently, achieving a grasping success rate of 75 % from 12 trials. Therefore, the system demonstrates significant potential in enhancing the capabilities of quadruped robots as service robots in real-world environments.

cs.RO↗

arXiv2025

Hand Gesture Recognition for Collaborative Robots Using Lightweight Deep Learning in Real-Time Robotic Systems

Direct and natural interaction is essential for intuitive human-robot collaboration, eliminating the need for additional devices such as joysticks, tablets, or wearable sensors. In this paper, we present a lightweight deep learning-based hand gesture recognition system that enables humans to control collaborative robots naturally and efficiently. This model recognizes eight distinct hand gestures with only 1,103 parameters and a compact size of 22 KB, achieving an accuracy of 93.5%. To further optimize the model for real-world deployment on edge devices, we applied quantization and pruning using TensorFlow Lite, reducing the final model size to just 7 KB. The system was successfully implemented and tested on a Universal Robot UR5 collaborative robot within a real-time robotic framework based on ROS2. The results demonstrate that even extremely lightweight models can deliver accurate and responsive hand gesture-based control for collaborative robots, opening new possibilities for natural human-robot interaction in constrained environments.

cs.RO↗