From c97b8a0d4136065fa3aa35ca520cd3e5d7844ba7 Mon Sep 17 00:00:00 2001 From: Sandun Ranasinghe <67415486+SandunGitHub@users.noreply.github.com> Date: Fri, 17 Jul 2026 10:12:30 +1000 Subject: [PATCH 1/3] Add Stage 2 proposal for Vision Based Cleaner Robot Added Stage 2 proposal for the Vision Based Cleaner Robot, including challenges, AI capabilities, software module allocation, and bill of materials. --- ...ndunRanasinghe-Vision-Based-Cleaning-Robot | 100 ++++++++++++++++++ 1 file changed, 100 insertions(+) create mode 100644 projects/SandunRanasinghe-Vision-Based-Cleaning-Robot diff --git a/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot b/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot new file mode 100644 index 0000000..ccf8aa7 --- /dev/null +++ b/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot @@ -0,0 +1,100 @@ +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 + +image + + +b. AI-System +i. Dust detection and mapping algorithm + +image + +2. Module Design + +image + +3. ROS2 Node graphs and Compute Allocation + +image + +## 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. + + + + + + + + From 1f682de2c57c627d2d8d7855cf1d49455d5cd082 Mon Sep 17 00:00:00 2001 From: Sandun Ranasinghe <67415486+SandunGitHub@users.noreply.github.com> Date: Fri, 17 Jul 2026 10:13:20 +1000 Subject: [PATCH 2/3] Delete projects/SandunRanasinghe-Vision-Based-Cleaning-Robot --- ...ndunRanasinghe-Vision-Based-Cleaning-Robot | 100 ------------------ 1 file changed, 100 deletions(-) delete mode 100644 projects/SandunRanasinghe-Vision-Based-Cleaning-Robot diff --git a/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot b/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot deleted file mode 100644 index ccf8aa7..0000000 --- a/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot +++ /dev/null @@ -1,100 +0,0 @@ -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 - -image - - -b. AI-System -i. Dust detection and mapping algorithm - -image - -2. Module Design - -image - -3. ROS2 Node graphs and Compute Allocation - -image - -## 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. - - - - - - - - From ec06c7104449a52aa4fd0123eaffc316bca25fa4 Mon Sep 17 00:00:00 2001 From: Sandun Ranasinghe <67415486+SandunGitHub@users.noreply.github.com> Date: Fri, 17 Jul 2026 10:13:58 +1000 Subject: [PATCH 3/3] Add Stage 2 proposal for Vision Based Cleaner Robot Added Stage 2 proposal for Vision Based Cleaner Robot, detailing challenges, AI capabilities, system architecture, software module allocation, and bill of materials. --- ...nRanasinghe-Vision-Based-Cleaning-Robot.md | 100 ++++++++++++++++++ 1 file changed, 100 insertions(+) create mode 100644 projects/SandunRanasinghe-Vision-Based-Cleaning-Robot.md diff --git a/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot.md b/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot.md new file mode 100644 index 0000000..ccf8aa7 --- /dev/null +++ b/projects/SandunRanasinghe-Vision-Based-Cleaning-Robot.md @@ -0,0 +1,100 @@ +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 + +image + + +b. AI-System +i. Dust detection and mapping algorithm + +image + +2. Module Design + +image + +3. ROS2 Node graphs and Compute Allocation + +image + +## 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. + + + + + + + +