A comprehensive exploration of Robot Operating System (ROS) capabilities through autonomous navigation in simulated environments. Demonstrates SLAM mapping, path planning algorithms, and real-time visualization using TurtleBot3 in Gazebo simulation.

Project Overview

The Simple Maze Navigation with ROS project demonstrates autonomous robot navigation in a simulated maze environment. This system integrates:

• ROS (Robot Operating System): Middleware for robot software development
• SLAM (Simultaneous Localization and Mapping): Real-time environment mapping as robot explores
• Path Planning: Autonomous route computation around obstacles
• RViz Visualization: Real-time 3D visualization of robot state, map, and planned paths
• Autonomous Exploration: Self-directed navigation through unknown environments

This project showcases fundamental robotics concepts essential for real-world autonomous systems, from basic navigation to advanced mapping and localization.

System Architecture

Overall Design Philosophy

The navigation system operates as an integrated pipeline:

Sensor Input (Simulated LIDAR)
    ↓
[Odometry Estimation]
    ↓
[SLAM Module - Localization & Mapping]
    ↓
[Occupancy Grid Creation]
    ↓
[Path Planning - Global & Local]
    ↓
[Motion Commands to Robot]
    ↓
[RViz Real-time Visualization]

Core Components

1. SLAM (Simultaneous Localization and Mapping)

Purpose: Build a map of the environment while simultaneously determining the robot’s location within it.

Key aspects:

• Localization: Determine robot position using sensor feedback
• Mapping: Build occupancy grid from sensor measurements
• Loop closure: Detect when robot revisits known areas
• Uncertainty management: Track confidence in position and map estimates

ROS Nodes Involved:

• /mapping node: Manages SLAM algorithm
• /odom node: Provides odometry data (wheel encoders, IMU)
• /map node: Publishes occupancy grid

2. Occupancy Grid

Representation: 2D grid where each cell represents probability of obstacle presence

Grid Cell Values:
-1: Unknown
 0: Free (traversable)
 1: Occupied (obstacle)
 
Example 10×10 grid:
[1][1][1][1][1][1][1][1][1][1]
[1][0][0][0][1][0][0][0][0][1]
[1][0][1][0][1][0][1][1][0][1]
[1][0][1][0][0][0][0][1][0][1]
[1][0][1][1][1][1][1][1][0][1]
[1][0][0][0][0][0][0][0][0][1]
[1][1][1][1][0][1][1][1][1][1]
...

3. Path Planning

Global Planner: Dijkstra or A* algorithm

• Computes optimal path from current position to goal
• Works on occupancy grid
• Provides high-level route guidance

Local Planner: Dynamic Window Approach (DWA)

• Real-time obstacle avoidance
• Smooth trajectory generation
• Reactive to moving obstacles

4. RViz Visualization

Real-time Display Components:

• Robot pose: Current position and orientation
• Occupancy map: Discovered environment
• Sensor data: LIDAR scans, camera views
• Planned path: Global path to goal
• Local costmap: Obstacles in immediate vicinity
• Particle cloud: Localization uncertainty (if using particle filter)

Navigation Pipeline

Maze Exploration Workflow

Phase 1: Initialization

1. Launch ROS master
2. Start SLAM node
3. Initialize robot at known position
4. Begin sensor reading
5. Launch RViz visualization

Phase 2: Autonomous Exploration

1. Robot moves forward avoiding detected obstacles
2. LIDAR continuously scans environment (30-40Hz)
3. SLAM processes new sensor data
4. Occupancy grid updates with discovered structure
5. Robot pose refined through sensor fusion

Phase 3: Goal-Directed Navigation

1. User specifies goal position in RViz
2. Global planner computes optimal path
3. Local planner generates smooth trajectory
4. Motion controller executes velocity commands
5. Robot follows path while avoiding obstacles

Phase 4: Map Refinement

1. Robot revisits previously explored areas
2. Loop closure detection triggered
3. Map error corrected
4. Localization confidence increases
5. Final map published for navigation

Key ROS Topics and Messages

Published Topics:

/map                 - OccupancyGrid (robot's understanding of environment)
/amcl_pose           - Estimated pose with covariance
/path                - Global planned path to goal
/local_costmap       - Local obstacle grid
/scan                - Raw LIDAR measurements

Subscribed Topics:

/cmd_vel            - Velocity commands (linear x, angular z)
/goal               - Goal position from user/navigation server
/initial_pose       - Robot starting position

Autonomous Navigation Components

1. Robot Model

• Type: Differential drive robot (2 independent wheels)
• Max velocity: 1.0 m/s (configurable)
• Max angular velocity: 2.0 rad/s
• Footprint: Circular with safety margin
• Sensors: LIDAR scanner (360° sweep, 30m range typical)

2. SLAM Algorithm Options

Common ROS SLAM Implementations:

gmapping:

• Grid-based FastSLAM
• Good for small-to-medium environments
• Computationally light
• Suitable for this maze scenario

Hector SLAM:

• Occupancy grid mapping
• Works without odometry (scan-matching only)
• Higher computational cost
• Better in challenging environments

Cartographer:

• Google’s advanced SLAM
• High performance for large-scale mapping
• Supports multiple sensor modalities
• Production-grade system

3. Navigation Stack Configuration

Local Planner: DWA (Dynamic Window Approach)

Velocity Search Space:
v_x ∈ [-0.5, 1.0]  m/s  (linear)
ω_z ∈ [-2.0, 2.0]  rad/s (angular)

Prediction Horizon: 3 seconds ahead
Time Step: 0.1 seconds
Trajectory Samples: 360 (one per degree)

Global Planner: A* or Dijkstra

Grid Resolution: 0.05m per cell
Planning Frequency: 1-5 Hz
Cost Function: Euclidean distance + obstacle proximity

4. Localization Methods

Adaptive Monte Carlo Localization (AMCL):

• Particle filter-based approach
• Tracks robot pose uncertainty
• Recovers from localization errors
• Converges quickly in known environments

Parameters:

• Particles: 2000-5000 (more = better accuracy, slower computation)
• Initial uncertainty: ±(0.5m, 0.5m, ±0.5rad)
• Update frequency: 25 Hz

Maze Simulation Environment

Simulated Maze Characteristics

Layout Features:

• Starting position: Defined entrance point
• Goal position: Target location deep in maze
• Corridors: Varying widths (0.5m - 2.0m typical)
• Dead ends: Must be recognized and backtracked
• Intersections: Decision points for path planning
• Walls: Obstacles with clearance for safe passage

Sensor Simulation

LIDAR Simulation Parameters:

Range:         0.1m - 30m
Resolution:    1° (360 rays)
Frequency:     40 Hz
Noise Model:   Gaussian (σ = 0.01m) + occasional errors
Occlusion:     Full simulation of obstacles blocking rays

Odometry Simulation:

Wheel Encoder Noise: Gaussian (σ = 0.01m)
IMU Noise:          Gaussian (σ = 0.01 rad/s)
Drift Rate:         ~0.5% per meter traveled

Technical Implementation

ROS Node Architecture

┌─────────────────────────────────────┐
│     Simulation (Gazebo)             │
│  - Physics engine                   │
│  - Sensor simulation (LIDAR)        │
│  - Robot actuators                  │
└──────────────┬──────────────────────┘
               │ /scan, /odom
┌──────────────▼──────────────────────┐
│  SLAM Node (gmapping/Cartographer)  │
│  - Map building                     │
│  - Pose estimation                  │
└──────────────┬──────────────────────┘
               │ /map, /tf
┌──────────────▼──────────────────────┐
│  Navigation Stack                   │
│  - move_base (navigation server)    │
│  - Global planner (A*)              │
│  - Local planner (DWA)              │
│  - Costmap builder                  │
└──────────────┬──────────────────────┘
               │ /cmd_vel
┌──────────────▼──────────────────────┐
│  Robot Controller                   │
│  - Actuator drivers                 │
│  - Velocity execution               │
└──────────────┬──────────────────────┘
               │ /odom feedback
┌──────────────▼──────────────────────┐
│  RViz Visualization                 │
│  - Real-time display                │
│  - User interaction (goal setting)  │
└─────────────────────────────────────┘

RViz Display Panels

Main Visualization Elements:

1. Map Display
   • Shows discovered occupancy grid
   • Color coding: Green (free), Red (occupied), Gray (unknown)
   • Updates in real-time as robot explores
2. Robot Representation
   • Arrow showing pose (position + orientation)
   • Footprint circle indicating physical extent
   • Coordinate frames visualization
3. Path Display
   • Global path: Full route from start to goal (blue line)
   • Local path: Immediate trajectory (green line)
   • Trajectory predictor: Future position prediction
4. Sensor Data
   • LIDAR scan points: Red/green clouds showing observations
   • Range limitations: Distant objects, occluded areas
   • Real-time updating at scan frequency
5. Costmap Layers
   • Static obstacles: Inflation radius around detected walls
   • Dynamic obstacles: Moving objects (if present)
   • Cost gradient: Distance from obstacles

Technical Challenges & Solutions

Challenge 1: Map Inconsistencies (Loop Closure)

Problem: When robot revisits an area, previous and new measurements may not align perfectly Solution: Loop closure detection and correction

• Feature matching between current and past scans
• Graph optimization to redistribute error
• Result: Consistent global map

Challenge 2: Localization Uncertainty

Problem: Odometry accumulates errors over distance Solution: Sensor fusion with AMCL

• Periodic resets using loop closure
• Particle filter refinement
• Covariance tracking for confidence estimation

Challenge 3: Dynamic Obstacle Avoidance

Problem: Local costmap may have stale information Solution: Real-time costmap updates

• High-frequency sensor input (40Hz)
• Exponential decay of old data
• Conservative expansion around obstacles

Challenge 4: Computational Performance

Problem: Path planning and mapping compete for resources Solution: Hierarchical planning approach

• Global planner (1-5Hz): Full map consideration
• Local planner (20-40Hz): Immediate obstacle response
• Costmap updates asynchronously

Project Demonstrations

Simulation Features

The ROS simulation demonstrates:

• ✅ Real-time maze exploration from unknown environment
• ✅ Continuous SLAM mapping showing maze structure emergence
• ✅ Successful autonomous navigation to specified goal
• ✅ Loop closure detection and map refinement
• ✅ Obstacle avoidance in corridors and intersections
• ✅ Multi-sensor integration (LIDAR odometry)

Visualization Capabilities

RViz provides interactive features:

• Real-time map viewing as robot explores
• Goal specification via click-and-drag
• Path visualization (global and local)
• Sensor data inspection
• Pose and costmap debugging
• Performance metric monitoring

Key Achievements

✅ Complete navigation stack: SLAM + planning + control fully integrated ✅ Robust maze solving: Handles complex maze structures with dead ends ✅ Real-time visualization: RViz provides full debugging and monitoring capability ✅ Modular architecture: Easy to swap SLAM algorithms, planners, and controllers ✅ Scalable design: Works with different robot models and environments

Future Enhancements

Algorithm Improvements

• Multi-hypothesis tracking: Handle kidnapped robot scenarios
• Semantic segmentation: Recognize room types and landmarks
• Long-term autonomy: Manage uncertainty over extended missions
• Collaborative mapping: Multiple robots building shared map

Sensor Integration

• Camera-based SLAM: Visual odometry and feature detection
• IMU fusion: Better orientation tracking
• Depth sensors: RGB-D mapping for 3D environments
• Thermal imaging: Heat signature navigation

System Enhancements

• Cloud connectivity: Map sharing and remote control
• Machine learning: Learning from exploration patterns
• Hardware deployment: Real robot validation
• Sim-to-real transfer: Domain adaptation for physical systems

Conclusion

The Simple Maze Navigation with ROS project demonstrates the complete autonomous navigation pipeline from sensor input to goal-directed robot motion. By leveraging ROS’s modular framework, the system showcases how SLAM, path planning, and real-time visualization combine to create a robust autonomous exploration system.

Key learning outcomes:

• Understanding ROS architecture and publish/subscribe model
• SLAM algorithm concepts and practical implementation
• Path planning algorithms in robotics
• Real-time constraint management
• Debugging and visualization with RViz

The simulation environment provides a safe, repeatable testbed for developing and validating autonomous navigation algorithms before deployment on real robots.

For complete system demonstration and real-time navigation visualization, see the simulation video in the gallery above.