SandunRanasinghe-Vision-based-Robot

projects/SandunRanasinghe-Vision-Based-Cleaning-Robot.md

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+Vision Based Cleaner Robot - Stage 2 Proposal 
+Participant : Sandun
+Stage Completed: 2
+Repository : https://github.com/SandunGitHub/D-Robotics/tree/main/Stage%202
+
+Stage 2 — Build up your own intelligent robot
+Challenge 2 – Build Challenge
+Challenges
+Scenario: 
+#### Operating environment and constraints
+-	Indoor floors such as flat floors, narrow corridors and doorways, furniture and dynamic obstacles, stairs and drop-offs.
+-	Reliable operation under vacuum cleaner green LED condition (20-50 lux).
+-	Inference performance:  >= 15 FPS for real-time dust detection.
+-	Maximum end-to-end latency: ~30 s  latency from image capture to power motor adjustment. 
+
+##### Who benefits and primary interaction mode
+-	Target Users: cleaners having disabilities, elderly individuals who may have difficulty in regular cleaning, non-commercial cleaning services.
+-	Interaction Mode: IR-based remote controller.
+-	Success Criterion: User can initiate a cleaning task and robot completes it without any further intervention.
+##### Core AI capabilities: 
+1.	Real-time dust and debris detection via a computer vision model.
+2.	Classify the dust density as low, medium, or high.
+3.	Intelligent adjustment of vacuum power based on the nature of cleaning load.
+Innovation/differentiation
+Adaptive cleaning strategy that balances cleaning performance, battery life and operational efficiency.
+#####  Success Criteria
+-	Dust detection accuracy: 
+-	Vacuum power adjustment latency:
+-	Battery power utilization:
+-	Cleaning task completion:
+-	Obstacle avoidance rate:
+
+Challenge 2 – AI System architecture
+1.	System flow diagram
+a.	Non-AI system
+i.	Path control and obstacle avoidance
+
+<img width="821" height="572" alt="image" src="https://github.com/user-attachments/assets/0c190fdf-7f1e-4bce-8116-53b245fee5bd" />
+
+
+b.	AI-System
+i.	Dust detection and mapping algorithm
+
+<img width="575" height="371" alt="image" src="https://github.com/user-attachments/assets/de849035-ca90-4e54-85ae-7f9a9390b685" />
+
+2.	Module Design
+
+<img width="940" height="770" alt="image" src="https://github.com/user-attachments/assets/2c40b081-ac3c-4400-bb7f-eb268a85632d" />
+
+3.	ROS2 Node graphs and Compute Allocation
+
+<img width="803" height="457" alt="image" src="https://github.com/user-attachments/assets/7a80e317-3e6d-4d69-a5a6-861876c26567" />
+
+## Software Module Allocation
+
+| Major Module | Execution Unit | Compute Unit | Real-Time Requirement | Recommended Timing / Priority |
+|--------------|----------------|--------------|-----------------------|-------------------------------|
+| YOLOv5 Dirt Detection | Separate ROS 2 process | BPU | Soft real-time | Process the newest frame; avoid building a frame backlog. |
+| Dirt Tracking / Decision Logic | Separate ROS 2 process (or callback within the detection node) | BPU | Soft real-time | Update movement decisions within **100–300 ms**. |
+| IR Decoder Node | Separate ROS 2 process with a GPIO-reading thread | Arduino Nano | Firm real-time | Decode button presses within **50–100 ms**. |
+| Ultrasonic Obstacle Sensor Node | Separate ROS 2 process | CPU | Firm real-time | Update at **10–20 Hz**; reject stale measurements. |
+| Command / Mode Manager | Separate ROS 2 process | CPU | Firm real-time | Select manual, autonomous, or emergency commands within **20–50 ms**. |
+| Motion Controller | Separate ROS 2 process | CPU | Firm real-time | Execute the control loop at **20–50 Hz** with low timing jitter. |
+| Motor Driver Interface | Dedicated thread or separate process | CPU | Firm real-time | Apply stop and direction commands within **20 ms**. |
+| Wheel Feedback / Encoder Reader *(optional)* | Dedicated thread | CPU | Firm real-time | Sample encoder data at **50–100 Hz**. |
+| ROS 2 Logging and Diagnostics | Shared background process/thread | CPU | Non-real-time | Must not interfere with sensing or motion control tasks. |
+| ROS 2 DDS Communication | Middleware-managed threads | CPU | Soft real-time | Use the default scheduler; consider CPU affinity only after system profiling. |
+
+Challenge 3 — Engineering Plan
+
+## Bill of Materials (BOM)
+
+| Item | Qty | Supplier / SKU | Voltage | Interface | Notes |
+|------|:---:|----------------|---------|-----------|------|
+| RDK X5 Development Board | 1 | D-Robotics RDK X5 | 5 V DC | USB, Ethernet, GPIO, MIPI CSI | Main embedded AI computer running Ubuntu and ROS 2 |
+| MicroSD Card (32–64 GB) | 1 | SanDisk Ultra / Samsung EVO | N/A | SDIO | Stores Ubuntu, ROS 2, and application software |
+| USB Camera | 1 | Logitech | 5 V | USB | Captures images for YOLOv5 dirt detection |
+| IR Receiver Module (38 kHz) | 1 | Keyestudio | 3.3–5 V | GPIO | Receives NEC infrared remote commands |
+| IR Remote Controller | 1 | Keyestudio | Battery | Infrared | Manual robot control |
+| DC Geared Motors | 4 | Robot Chassis Motors | 6–12 V | PWM | Drives the four-wheel mobile platform |
+| RoboClaw Motor Driver | 1 | RoboClaw 2×15A | 6–30 V | UART | Controls left and right motor pairs |
+| 4WD Robot Chassis | 1 | Acrylic/Metal Chassis | N/A | Mechanical | Supports all electronics and mechanical components |
+| Wheels | 4 | Compatible with Chassis | N/A | Mechanical | Four drive wheels |
+| Battery Pack | 1 | Li-ion/LiPo | 12 V | Power | Primary power source |
+| DC–DC Buck Converter | 1 | LM2596 Module | 12 V → 5 V | Power | Supplies regulated 5 V power to the RDK X5 |
+| Connecting Wires | 1 Set | Dupont/JST Kit | N/A | GPIO / Power | Electrical connections between modules |
+| Mounting Hardware | 1 Set | M3 Screws, Nuts, Standoffs | N/A | Mechanical | Secures electronic and mechanical components |
+
+
+Link to the timeline : https://github.com/SandunGitHub/D-Robotics/blob/main/Stage%202/ROADMAP.md
+
+Please note that due to late arrival of some products the project repo is still being developed and soon will be updated under Stage 2/project/ on 19th of July 2026.
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