Autonomous Navigation and Mapping in Agriculture.pptx

Autonomous

1. 1. Path Planning and Obstacle Avoidance Algorithms Objective:  Enable robots to navigate fields efficiently while avoiding obstacles (rocks, trees, animals). Key Algorithms: A (A-Star) Algorithm * Finds the shortest path between two points. Used in  autonomous tractors  for row-to-row navigation. Dijkstra’s Algorithm Computes optimal paths in grid-based maps. Applied in  greenhouse robots  for structured environments. RRT (Rapidly-exploring Random Trees) Efficient for dynamic, unstructured fields. Used in  weed-removal robots  to navigate around unpredictable obstacles.

2. 1. Path Planning and Obstacle Avoidance Algorithms… Potential Fields Method Attracts robots toward goals while repelling from obstacles. Helps  sprayer drones  avoid collisions with trees. Challenges: Dynamic obstacles  (e.g., animals, moving machinery). Uneven terrain  affecting wheeled robots.

3. 2. Localization and Mapping Techniques Objective:  Allow robots to know their position and create maps of farmland. Localization Methods: GPS/GNSS (RTK-GPS for centimeter -level accuracy) Used in  autonomous tractors  (e.g., John Deere’s AutoTrac ). Visual Odometry (VO) / LiDAR Odometry Cameras/LiDAR estimate movement when GPS is unreliable (e.g., under tree canopies). IMU (Inertial Measurement Unit) Provides short-term position tracking when sensors fail.

4. 2. Localization and Mapping Techniques… Mapping Techniques: SLAM (Simultaneous Localization and Mapping) LiDAR SLAM:  Creates 3D maps for  orchard robots . Visual SLAM (VSLAM):  Used in  greenhouse robots  (e.g., Harvest Automation). Grid Mapping (Occupancy Grids) Divides fields into navigable vs. obstructed areas. Challenges: GPS-denied environments  (e.g., dense crops, indoor farms). Changing landscapes  (growing crops alter maps).

5. 3. Integration of GPS and GIS in Agricultural Robotics GPS (Global Positioning System): Provides real-time location data. Applications: Auto-steering tractors  (e.g., Case IH Autopilot). Drone-based field scanning  for precision agriculture. GIS (Geographic Information Systems): Stores and analyzes spatial data (soil quality, moisture, yield maps). Applications: Variable-rate seeding/fertilization  (matching robot actions to soil data). Crop health monitoring  (overlaying drone images with GIS maps).

6. 3. Integration of GPS and GIS in Agricultural Robotics Integration Benefits: Precision Farming:  Combines GPS-guided robots with GIS data for optimized field operations. Data-Driven Decisions:  Robots adjust tasks (e.g., watering, spraying) based on GIS layers. Example Workflow: GPS  guides a drone to scan a field. GIS   analyzes the data, identifying drought-stressed zones. Autonomous irrigation robot  targets only those areas.

7. Summary Table: Key Technologies in Agri-Navigation Technology Use Case Example Robots A*/RRT Path Planning Row-following, obstacle avoidance Naïo’s Oz weeding robot LiDAR SLAM Orchard/vineyard mapping Burro’s autonomous carts RTK-GPS + GIS Precision planting/spraying John Deere’s ExactEmerge planter Future Trends AI-enhanced navigation:  Deep learning for adaptive path planning. Swarm robotics:  Multiple robots sharing maps in real-time. 5G-enabled farming:  Ultra-precise GPS corrections for centimeter-level accuracy.