People living near garbage dumps and cities find waste everywhere. The reason behind this is the waste is not segregated at source and is scattered instead of being treated
Create a mechanism which will help me sort out the garbage in wet and dry form of garbage at the source
Neemuch, MP, India
Solution
| Component | Description |
|---|---|
| Technical Components | ### **Comprehensive Analysis: Autonomous Grass-Cutting Robot** #### **1. Technical Components Needed** | **Component** | **Purpose** | **Constraints/Requirements** | |------------------------------|------------|------------------------------| | **Robot Chassis (Wheeled/Tracked)** | Base mobility | Slow speed (<5 km/h), durable for outdoor terrain | | **GPS-RTK Module** | Precision navigation (±2 cm accuracy) | Must prevent deviation from paths | | **LiDAR/Ultrasonic Sensors** | Obstacle avoidance | Ensure it avoids unintended roaming | | **Grass-Cutting Mechanism (Motorized Blades)** | Primary task execution | No pesticides, adjustable cutting height | | **Solar Panel + Battery Pack** | Autonomous charging | Weatherproof, sufficient capacity for daily operation | | **360° Camera + Night Vision** | Reconnaissance | Live feed transmission, cloud storage integration | | **4G/5G Modem** | Remote communication | Stable connectivity at 150 km | | **Microcontroller (Raspberry Pi/NVIDIA Jetson)** | Central control | Prevents prohibited actions (rain, battery shutdown) | | **IMU (Inertial Measurement Unit)** | Stability on slopes | Avoid tipping | | **Weather Sensor** | Rain detection | Force return to charging station | --- #### **2. Recommended Tech Stack** | **Category** | **Technology/Model** | |--------------------|----------------------| | **Navigation** | ROS 2 + RTK GPS (Ardupilot) | | **Sensors** | LiDAR (RPLIDAR A3), Ultrasonic (HC-SR04) | | **Cameras** | Reolink 4K PoE (for surveillance) | | **Processing** | NVIDIA Jetson Orin (Edge AI) | | **Connectivity** | 4G LTE Router (Huawei B535) | | **Energy** | 200W Solar Panel + 48V LiFePO4 Battery | | **Cloud** | AWS IoT Core (remote control) | | **Storage** | 1TB SSD (local footage), AWS S3 (backup) | --- #### **3. Implementation Steps** **Phase 1: Hardware Assembly** - **Step 1:** Build chassis with waterproof wheels/tracks. - **Step 2:** Mount GPS-RTK, LiDAR, and cameras for environmental awareness. - **Step 3:** Integrate grass-cutting motors with adjustable height control. **Phase 2: Software Configuration** - **Step 4:** Program path-following (Waypoint-Following Algorithm). - **Step 5:** Train computer vision (YOLOv5) to identify excess grass and obstacles. - **Step 6:** Set rain/battery thresholds (e.g., shutdown if rain > 2 mm/h or battery < 15%). **Phase 3: Connectivity & Cloud** - **Step 7:** Configure AWS IoT for remote commands and live video feeds. - **Step 8:** Develop mobile dashboard (React Native app for iOS/Android). **Phase 4: Testing** - **Step 9:** Validate autonomy in smaller zones before full 15-acre deployment. --- #### **4. Required Technical Learning** | **Skill** | **Resources** | |----------------------|--------------| | ROS Navigation | Official ROS tutorials | | Precision GPS | Ardupilot documentation | | Edge AI Vision | NVIDIA DeepStream SDK | | Solar Power Mgmt | Victron Energy guides | | AWS IoT Core | AWS Free Tier labs | --- #### **5. Budget Calculation** | **Category** | **Item** | **Cost (USD)** | |----------------------|----------|----------------| | **Hardware** | Chassis + Motors | $3,000 | | | NVIDIA Jetson Orin | $1,200 | | | RTK-GPS (Hex here2) | $800 | | | Solar + Battery | $1,500 | | | LiDAR + Sensors | $600 | | **Software** | AWS IoT Core (1 yr) | $300 | | | Computer Vision License | $150 | | **Maintenance** | Spare Parts (1 yr) | $500 | | **Total** | | **$8,050** | **Annual Recurring Costs:** - Cloud Services: $450 - Mobile Data: $300 - Battery Replacement: $200 **Grand Total (Year 1): $8,050 + $950 = ~$9,000** --- **Conclusion:** The solution combines **slow-moving autonomy**, **self-charging**, and **surveillance** while respecting constraints (rain, pesticides, path adherence). Costs are justified by durability and scalability (expandable to larger farms). |
| Key Features |
Feature: Autonomous roving
Format: Software based routing Usage: Autonomous |
| Implementation Steps | The implementation should happen linearly as each feature |
Mahimna12#
Rated: 4 stars
Review: Great go ahead !