Obstacle and Pit Avoidance System

This project implements an autonomous navigation system for robots, focusing on obstacle avoidance and pit detection. The system uses Intel RealSense D455 for point cloud data, processes this data using the Point Cloud Library (PCL), and makes real-time decisions for obstacle avoidance. It also handles odometry to return to the original path after avoiding obstacles.

📁 Project Structure

├── avoidance.py # Python script for avoidance logic using odometry ├── pit_detection.cpp # C++ code for detecting pits ├── obstacle_detection.cpp # C++ code for detecting physical obstacles

🚀 Overview

This project is designed to allow a robot to autonomously navigate around obstacles and pits using the following approach:

1. Obstacle Detection:

• Point Cloud Generation: The system uses the Intel RealSense D455 camera to publish 3D point cloud data.
• Point Cloud Processing: The point cloud data is processed using the PCL (Point Cloud Library) workspace to detect obstacles.
• Avoidance Logic: Once an obstacle is detected, the robot will avoid it by altering its trajectory based on the detected obstacle's distance and position.

2. Pit Detection:

• Similar to obstacle detection, but focuses on detecting sudden drops in terrain (like cliffs or holes), which could cause the robot to fall.

3. Odometry and Path Recovery:

• The system uses odometry data to ensure that after avoiding an obstacle or pit, the robot can return to the original path, maintaining its mission objective.