Autonomous Navigation and 3D Mapping for Grass-Cutting Robots on Inclined Terrains

Project Overview

This project enhances the autonomy of a grass-cutting robot (Clearpath Moose) to operate on uneven, hilly terrain. The system addresses slope hazards through three core software components:

1. Extended Kalman Filter (EKF): Fuses wheel odometry with compass data for accurate localization.
2. 3D LiDAR Mapping: Builds an OctoMap-based occupancy grid to represent complex terrain.
3. Slope-Aware Navigation: Uses an A* planner with a custom cost function to identify and avoid hazardous gradients (>20°) while maintaining coverage.

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Main Source Code

The core logic for this project is found in the following file:

.../moose/moose/controllers/moose_path_following/moose_path_following.c

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Prerequisites

• Webots R2022a

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System Architecture

1. Kinematics & Low-Level Control

• Modified Hardware Interface: Custom Moose.proto with 8-wheel PositionSensors enabled closed-loop odometry.
• Differential Drive Logic: Converts raw encoder ticks into metric state estimates (x, y, θ) using skid-steering kinematics.

2. Sensor Fusion (EKF)

• State Prediction: Propagates system uncertainty (Covariance Matrix P) based on motion model.
• Update Step: Fuses magnetometer (compass) yaw with the prediction to eliminate odometric drift.
• Auto-Calibration: Solves coordinate frame mismatch by aligning odometry θ with the initial compass reading.

3. 3D Mapping (OctoMap)

• LiDAR Pipeline: Filters point clouds (0.3m < d < 50m) and downsamples using a 2003 voxel grid.
• Occupancy Grid: Probabilistic log-odds update model with dynamic map recentring for unbounded exploration.
• Traversability Analysis: Converts 3D data into a 2D cost map, penalizing surface roughness and slopes.

4. Navigation & Planning

• Slope-Aware A:* Global planner that treats gradients > 20° as obstacles.
• Hazard Detection: High-frequency (32ms) safety layer that monitors IMU Roll/Pitch to prevent tipping.
• Stuck Recovery: Monitors displacement over 5s windows; triggers reversing manoeuvres if the robot is physically stuck.

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Authorship & Contributions

This project was a collaborative effort. The specific contributions for assessment purposes are listed below.

Yichang Chao

Role: Robot Modelling, Low-level Control & Kinematics File: protos/Moose.proto, controllers/moose_path_following/moose_path_following.c Key Contributions:

• Modified Moose.proto to add 8-wheel feedback sensors.
• Implemented low-level C driver for motor/sensor interfacing.
• Developed Differential Drive Odometry and EKF Prediction step.

Ran Zhang

Role: Sensor Fusion, Calibration & Validation File: controllers/moose_path_following/moose_path_following.c Key Contributions:

• Implemented EKF Update step (Compass fusion).
• Developed the "Auto-Alignment" routine to fix coordinate frame inversion.
• Built the GPS Ground Truth validation system.

Piero Flores López

Role: 3D Mapping & Environment Reconstruction File: controllers/moose_path_following/moose_path_following.c Key Contributions:

• Built the LiDAR processing pipeline (Filtering, Voxel Grid, Transform).
• Implemented OctoMap-style occupancy grid and ICP Scan Matching.
• Developed the Traversability Cost function (Slope + Roughness).

Inika Kumar

Role: Path Planning, Hazard Detection & Evaluation File: controllers/moose_path_following/moose_path_following.c Key Contributions:

• Implemented the slope-aware A* global planner.
• Developed the IMU-based Hazard Detection safety layer.
• Integrated the Navigation Controller (Path Following + Performance Metrics).