Autonomous Multi Terrain RASP Rover Navigation PyBullet

Overview

This project presents an autonomous terrain-aware rover navigation system developed in PyBullet. The rover traverses multiple terrain types while avoiding obstacles using the A* path planning algorithm.

The simulation demonstrates important robotics concepts including:

• Path Planning
• Obstacle Avoidance
• Terrain-Aware Navigation
• Autonomous Mobility
• Robotics Simulation

The project is inspired by the RASP Rover platform and serves as a digital testbed for autonomous navigation algorithms.

---

Features

• A* Path Planning

• Obstacle Avoidance

• Multi-Terrain Navigation

• Smooth Terrain Traversal

• Rough Terrain Traversal

• Slope Climbing

• Terrain-Aware Speed Adaptation

• 4-Wheel Rover Simulation

• PyBullet Physics Environment

---

System Architecture

Environment
     ↓
Terrain Analysis
     ↓
Localization
     ↓
A* Path Planner
     ↓
Waypoint Generation
     ↓
Motion Controller
     ↓
Rover Navigation

---

Terrain Types

Smooth Terrain

Represents flat tiled surfaces where the rover can travel at higher speeds.

Rough Terrain

Represents uneven ground that requires slower traversal.

Slope Terrain

Represents inclined terrain requiring controlled ascent.

---

Path Planning

The rover uses the A* search algorithm to determine an optimal path from the start position to the goal position.

The evaluation function is:

f(n) = g(n) + h(n)

Where:

• g(n) = Actual cost from start node
• h(n) = Heuristic estimate to goal
• f(n) = Total estimated cost

Euclidean Distance is used as the heuristic function.

---

Simulation Environment

The environment contains:

• Multiple terrain zones
• Static obstacles
• Start position
• Goal position
• Planned path visualization

---

Demo

The simulation demonstrates autonomous navigation of a 4-wheel rover across multiple terrain types using A* path planning.

Demonstrated Features

• Generation of an optimal path using A* search
• Obstacle avoidance in a grid-based environment
• Terrain-aware navigation across:
  • Smooth terrain
  • Rough terrain
  • Sloped terrain
• Adaptive traversal speed based on terrain conditions
• Goal-directed autonomous navigation

Simulation Demo

---

Results

The rover successfully:

• Avoids obstacles
• Generates collision-free paths
• Traverses multiple terrain types
• Reaches the target location autonomously

---

Technologies Used

• Python
• PyBullet
• NumPy
• Robotics Algorithms
• A* Path Planning

---

Future Work

• SLAM Integration
• IMU-Based Localization
• Camera-Based Obstacle Detection
• ROS2 Integration
• RRT* Path Planning Comparison
• Real Hardware Deployment on RASP Rover

---

Installation

Clone the repository:

git clone https://github.com/yourusername/Autonomous-Terrain-Aware-Rover-Navigation.git
cd Autonomous-Terrain-Aware-Rover-Navigation

Install dependencies:

pip install -r requirements.txt

Run the simulation:

python src/main.py

---

Author

Ananya

Robotics and AI