Field Robotics Forestry Robot locomotion Forestry Automation SLAM in Forestry
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Forestry Robot locomotion Forestry Automation SLAM in Forestry Autonomous Robots for Silviculture and treatment Broad Application: Automatic Guidance Sowing, Weeding, Weeding, Spraying, Broad-acre harvesting Horticulture: picking of fruits Robot milking, Sheep Sheering Slaughtering, Live stock Inspection Robots in construction Unsolved problems in construction
Field Robotics Forestry Robot locomotion Forestry Automation SLAM in Forestry
- 1. UNIT-2 Field Robotics Prepared by- Dr. Mohd Aslam PhD. in Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav, Kolhapur Maharashtra India 416121.
- 2. Contents • Forestry • Robot locomotion • Forestry Automation • SLAM in Forestry • Autonomous Robots for Silviculture and treatment • Broad Application: Automatic Guidance • Sowing, Weeding, Weeding, Spraying, Broad-acre harvesting • Horticulture: picking of fruits • Robot milking, Sheep Sheering • Slaughtering, Live stock Inspection • Robots in construction • Unsolved problems in construction SITCOE YADRAV 2
- 3. SITCOE YADRAV Forestry • In field robotics, "forestry definitions" refers to the application of robotics in forestry practices, encompassing areas like environmental preservation, wildfire management, inventory, and forest operations like planting, pruning, and harvesting. 3
- 4. SITCOE YADRAV Robot locomotion • In the context of forestry robotics, robotic locomotion are the various methods robots use to move and navigate through forest environments, including wheeled, legged, or other specialized systems, to perform tasks like harvesting, transport, or terrain assessment. • Types of Locomotion: • Wheeled: Many current forestry machines use wheels for locomotion, which are energy-efficient and simple to control. • Legged: For mountainous or very challenging terrain, legged robots (quadruped, etc.) are being explored, mimicking animal movement for stability and maneuverability. • Other: Other specialized systems, such as tracks or articulated bodies, may also be used depending on the specific application and terrain. 4
- 5. SITCOE YADRAV • 1. Terrestrial Locomotion (Land) • Walking – e.g., humans, dogs, elephants • Running – e.g., cheetahs, horses • Jumping (Saltation) – e.g., frogs, kangaroos • Crawling – e.g., snakes, worms • Climbing – e.g., monkeys, lizards • 2. Aerial Locomotion (Air) • Flying – e.g., birds, bats, insects • Gliding – e.g., flying squirrels, gliding lizards • 3. Aquatic Locomotion (Water) • Swimming – e.g., fish, whales, ducks • Jet Propulsion – e.g., squids, jellyfish • 4. Arboreal Locomotion (Tree Movement) • Swinging (Brachiation) – e.g., gibbons, monkeys • Climbing – e.g., sloths, chameleons • 5. Cursorial Locomotion (Fast Running) • Found in animals adapted for speed, e.g., cheetahs, antelopes • 6. Fossorial Locomotion (Burrowing) • Digging and moving underground, e.g., moles, earthworms • 7. Parasitic Locomotion • Movement within or between hosts, e.g., tapeworms, leeches 5
- 6. SITCOE YADRAV Forestry Automation • Forestry automation involves using technology and robotics to streamline and automate tasks in forestry operations, such as harvesting, transporting logs, and managing forests, aiming for increased efficiency and sustainability. • Key Areas of Automation in Forestry: • Harvesting: • Autonomous Harvesters: Machines that can fell, process, and load trees onto forwarders without human intervention. • Operator Support Systems: AI-powered systems that assist operators with tasks like tree detection, positioning, and classification, providing real-time feedback and inventory reports. • Log Transport: • Autonomous Forwarders: Machines that can transport logs from the forest to the landing or processing plant without human control. • Direct-Loading Systems: Harvesters load logs directly onto autonomous forwarders, eliminating the need for manual loading. • Load-Changing Systems: A manned harvester cuts, processes, and places processed trees directly into its own bunk. • Forest Management: • Drones and Remote Sensing: Using drones and satellite imagery to monitor forest health, detect wildfires, and assess timber resources. • Forestry Software: Digital tools for inventory tracking, forest health monitoring, timber harvest planning, and compliance with environmental regulations. • Other Applications: • Autonomous Carts: Small, autonomous vehicles that move between a geo-spatially fixed unloading area and a GPS tracker on the harvester. • Smart Forests: Using AI, machine learning, sensor networks, and drones to detect and respond to real-time forest events, especially wildfires. 6
- 7. SITCOE YADRAV Benefits of Forestry Automation • Increased Efficiency: Automated systems can perform tasks faster and more reliably than humans, leading to higher productivity. • Reduced Costs: Automation can lower labor costs and improve resource utilization. • Improved Safety: Automated machines can reduce the risk of accidents for human workers. • Sustainability: Automation can help optimize resource management and reduce environmental impact. • Data-Driven Decision Making: Automated systems can collect and analyze data, providing valuable insights for better forest management. 7
- 8. SITCOE YADRAV SLAM in Forestry • In forestry, SLAM (Simultaneous Localization and Mapping) technology, particularly using LiDAR or visual sensors, helps create detailed 3D maps of forests, enabling accurate positioning of trees, reconstruction of the forest environment, and identification of tree species distribution, overcoming challenges of GPS signal loss in dense canopies. • SLAM is a process where a device simultaneously builds a map of an unknown environment and determines its own location within that map. 8
- 9. SITCOE YADRAV APPLICATION OF SLAM IN FORESTRY • Accurate Positioning: SLAM algorithms, especially those using LiDAR or visual technologies, are crucial for outdoor spatial positioning and mapping, especially where GPS signals are unreliable due to tree canopy obstruction. • 3D Forest Reconstruction: SLAM enables the creation of detailed 3D models of forests, allowing for the visualization and analysis of forest structure and resources. • Tree Mapping and Inventory: SLAM-based LiDAR technology can accurately map individual trees, their locations, and other parameters like height, diameter, and canopy coverage. • Forest Health Monitoring: By creating and updating 3D models, SLAM facilitates the monitoring of forest health and changes over time. 9
- 10. SITCOE YADRAV Working of SLAM • Sensors: SLAM systems typically utilize sensors like LiDAR (Light Detection and Ranging) or visual cameras to capture data about the environment. • Data Processing: The captured data is processed using SLAM algorithms to create a map and determine the device's location. • Mobile LiDAR Scanners: Mobile LiDAR scanners (MLIS) are often used in forestry for SLAM measurements, allowing for faster and more efficient mapping compared to traditional methods. 10
- 11. SITCOE YADRAV Benefits of using SLAM in forestry: • Efficiency: SLAM-based methods can map complex environments much faster than traditional methods. • Accuracy: SLAM provides accurate 3D maps and tree data, enabling better forest management and resource planning. • Automation: SLAM can be used to automate tasks like forest inventory and mapping, reducing labor costs and improving efficiency. 11
- 12. SITCOE YADRAV Examples of SLAM applications in forestry: • Mapping forest plots: SLAM can be used to create detailed maps of forest plots, including tree positions, heights, and diameters. • Forest harvester localization: SLAM can be used to localize forest harvesters and generate tree maps for navigation and operation. • Tree species identification: SLAM can be used to identify tree species based on their visual characteristics or LiDAR data. • Forest health monitoring: SLAM can be used to monitor changes in forest health and structure over time. 12
- 13. SITCOE YADRAV Challenges: • Data processing and storage: SLAM systems generate large amounts of data, which requires efficient processing and storage. • Sensor accuracy: The accuracy of SLAM systems depends on the accuracy of the sensors used. • Environmental conditions: SLAM systems can be affected by environmental conditions such as weather and lighting. 13