Researh Projects

Research Projects | NHURO Nexus

📂 Master SDK Source Hierarchy (Base Structure)

Students must adhere to this architecture. New nodes and interfaces should be integrated into the specified directories to ensure system compatibility.

nhuro_project/
├── voice_bridge.py             <-- (T1) Sensory Gateway (System Python)
└── nhuro_ws/                   <-- ROS 2 Workspace (Mamba Env)
    └── src/
        ├── nhuro_interfaces/   <-- ADD NEW .msg / .action FILES HERE
        │   ├── action/ Wave.action, Walk.action
        │   └── msg/
        └── nhuro_driver/       <-- ADD NEW RESEARCH NODES HERE
            └── nhuro_driver/
                ├── voice_parser_node.py   <-- (T2) The Brain
                ├── nhuro_action_node.py   <-- (T3) The Muscles
                └── nhuro/                 <-- Hardware Library
                    ├── robot.py
                    └── bus_servo.py

Detailed setup instructions are available in Module 0: Nexus Initialization.

🛠️ Available Laboratory Resources:

Sensors: Raspberry Pi Camera Module, USB Microphones, MPU6050 IMU.
Actuators: Bus-Servos (ttyAMA0), Motorized Active Grasper (Replacement Kit).
Software Environment: ROS 2 Humble, PyTorch, YOLOv8-tiny, Librosa.

AI & COMPUTER VISION

NHURO-Vision: Edge-AI Based Real-Time Object Recognition and Tracking for Humanoid Robotic Security

Problem Description

This project addresses the limitation of hard-coded environmental sensing. Students must enable NHURO to identify and track dynamic objects in real-time using limited onboard compute.

Project Blueprint & New Files:

nhuro_interfaces/msg/Detection.msg: Define custom data types for object labels and bounding boxes.
nhuro_driver/vision_node.py: Implement a node that wraps the YOLOv8-tiny inference engine.
Design Hint: Optimize the model for the Raspberry Pi and bridge output to the nhuro_voice_text topic.

Evaluation Standards: Inference Speed (FPS), Mean Average Precision (mAP), and CPU utilization.

References: 📄 You Only Look Once: Unified, Real-Time Object Detection 📄 YOLO: Real-Time Object Detection Framework 📄 Energy-Efficient Optimal Mode Selection for Edge AI Inference via Integrated Sensing-Communication-Computation (IEEE Transactions) 📄 Design and Control of a Small Humanoid Equipped With Flight Unit and Wheels for Multimodal Locomotion

CONTROL & KINEMATICS

NHURO-Dance: Development of a Rhythmic Motion Synchronization Framework for Multimodal Humanoid Interaction

Problem Description

The challenge is to align high-latency ROS 2 action execution with low-latency audio beats to create fluid, rhythmic entertainment routines.

Project Blueprint & New Files:

nhuro_driver/choreography_node.py: Create an Action Client that sequences multiple goals (Wave, Walk, Bow).
nhuro_driver/audio_analyzer.py: Implement real-time beat detection using librosa.
Design Hint: Adjust action step_duration parameters dynamically based on detected BPM.

Evaluation Standards: Rhythmic correctness (ms deviation from beat) and transition smoothness between discrete actions.

References: 📄 Key Pose-Based Dynamic Humanoid Motion Generation Using Parallel Multi-Fidelity Model Predictive Control (IEEE) 📄 ROS 2 Control: Synchronizing Multiple Actuators 📄 Autonomous Humanoid Robot Dance Generation System 📄 Dance Motion Control of a Humanoid Robot Based on Real-Time Tempo Tracking from Musical Audio Signals

ASSISTIVE MANIPULATION

NHURO-Care: Integration and Force-Limited Control of an Active Bionic Grasper for Assistive Humanoid Tasks

Problem Description

This project focuses on the integration of a motorized active grasper to allow for physical interaction with light objects like medicine bottles.

Project Blueprint & New Files:

nhuro_interfaces/action/Grasp.action: Define the goal (open/close) and feedback (pressure/torque).
nhuro_driver/nhuro/robot.py: Update the hardware library with a move_grasper() method.
Design Hint: Physically swap the static hand for the motorized replacement kit and map the new Servo ID.

Evaluation Standards: Grasp success rate (%), load-bearing capacity (grams), and torque accuracy to prevent crushing objects.

Academic References: 📄 Design of Low-Cost Bionic Grippers for Assistive Robots 📄 Force Control in Humanoid Hand Manipulation (IEEE) 📄 Multifingered force-aware control for humanoid robots 📄 Multifingered Robot Hand Compliant Manipulation Based on Human‑in‑the‑Loop Learning‑Control

NEURAL NETWORKS & CONTROL

NHURO-Brain: Design of an Artificial Neural Network Controller for Dynamic Gait Optimization in Bipedal Humanoids

Problem Description

Hard-coded patterns are unstable on uneven terrain. The student must develop a neural controller that adjusts the gait dynamically based on IMU feedback.

Project Blueprint & New Files:

nhuro_driver/imu_listener_node.py: Collect orientation data ($pitch$, $roll$) from the MPU6050.
nhuro_driver/neural_gait_node.py: Run a trained ANN to predict optimal servo angle offsets.
Design Hint: Train the model using PyTorch and deploy for real-time inference on the Pi.

Evaluation Standards: Balance stability (variance in IMU data), inference latency (ms), and successful walk distance without falling.

Academic References: 📄 Design of central pattern generator for humanoid robot walking based on multi-objective GA (IEEE) 📄 ZNN‑Based Gait Optimization for Humanoid Robots with ALIP and Inequality Constraints 📄 Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control 📄 Bipedal Robot Gait Control Strategies Based on Reinforcement Learning

HUMAN-ROBOT INTERACTION

NHURO-Interface: Development of a Low-Latency Web Dashboard and Digital Twin for Remote Humanoid Telemetry

Problem Description

Students must build a low-latency web dashboard to visualize "internal thoughts" (voice logs) and "physical state" (step count).

Project Blueprint & New Files:

~/nhuro_project/dashboard/app.js: Implement a WebSocket client using roslibjs.
~/nhuro_project/dashboard/index.html: Design a visual UI to show step counts and voice logs.
Design Hint: Use the rosbridge_suite to communicate between the browser and the ROS workspace.

Evaluation Standards: UI Refresh rate (Hz), data synchronization accuracy, and cross-compatibility.

Academic References: 📄 Introducing Robotics UI: A Web Interface Solution for ROS 2 Robots 📄 Documentation: Robot Web Tools (Humble) 📄 Low‑Latency Communications for Digital Twin Empowered Systems 📄 2. High‑Speed and Impact‑Resilient Teleoperation of Humanoid Robots

ROBOTIC DYNAMICS & SAFETY

NHURO-Safe: Autonomous Fall Detection and Recovery Strategies for Humanoid Robots via Inertial Sensory Feedback

Problem Description

Bipedal robots are prone to falling. The student must detect falls and execute autonomous "Stand-Up" sequences via IMU monitoring.

Project Blueprint & New Files:

nhuro_driver/fall_manager_node.py: Monitor pitch/roll thresholds to detect balance loss.
nhuro_interfaces/action/StandUp.action: Define the multi-phase stand-up sequence.
Design Hint: Develop high-torque servo patterns to safely return the robot to a neutral pose.

Evaluation Standards: Detection latency (ms), success rate of stand-up recovery, and prevention of servo overheating.

Academic References: 📄 Fall Detection and Standing Up Strategy for Humanoids 📄 Reflex Balance Schemes and Fast Fall Impact Mitigation Mechanisms 📄 Multi‑Phase Trajectory Optimization for Post‑Fall Autonomous Humanoid Recovery 📄 Whole‑Body Control for Humanoid Fall Mitigation

← Return to NHURO Nexus Portal

NHURO Research Sandbox

NHURO-Vision: Edge-AI Based Real-Time Object Recognition and Tracking for Humanoid Robotic Security

NHURO-Dance: Development of a Rhythmic Motion Synchronization Framework for Multimodal Humanoid Interaction

NHURO-Care: Integration and Force-Limited Control of an Active Bionic Grasper for Assistive Humanoid Tasks

NHURO-Brain: Design of an Artificial Neural Network Controller for Dynamic Gait Optimization in Bipedal Humanoids

NHURO-Interface: Development of a Low-Latency Web Dashboard and Digital Twin for Remote Humanoid Telemetry

NHURO-Safe: Autonomous Fall Detection and Recovery Strategies for Humanoid Robots via Inertial Sensory Feedback