v1.0.2 - Final Repository Polish

v1.0.2

Final repository-polish release.

Improvements

• Clarified shared simulation time versus individual robot completion time
• Updated the README results-file structure
• Fixed MATLAB CI requirements formatting
• Cleaned MATLAB source-code comments
• Removed duplicate and project-relative comments

Validation

• MATLAB CI smoke test passes on the final main branch
• Example planning and navigation metrics remain unchanged
• No navigation, planning, DWA, LiDAR, or SLAM algorithm behavior was intentionally changed

This release represents the final polished repository state.

v1.0.1 - Documentation and CI Polish

v1.0.1

Maintenance release focused on repository quality, documentation, and continuous integration.

Improvements

• Added MATLAB GitHub Actions CI workflow
• Added non-interactive MATLAB smoke testing
• Added MATLAB CI status badge
• Added GitHub release badge
• Added MIT license badge
• Updated repository structure documentation
• Clarified the MATLAB CI test environment
• Added repository topics and social preview metadata
• Improved README consistency and project presentation

CI

The repository now runs a MATLAB R2024b smoke test on pushes and pull requests to main.

The CI validates:

• MATLAB source file availability
• Project dependency resolution
• Shared default configuration
• Differential-drive kinematics sanity checks

Notes

The full interactive five-algorithm navigation simulation is not executed in CI because it requires interactive goal selection and graphical simulation. Users can run the complete project locally from MATLAB using:

main

v1.0.0 - Multi-Algorithm Robot Navigation with Live SLAM

v1.0.0

Initial stable release of the MATLAB multi-algorithm robot navigation and live SLAM comparison project.

Highlights

• Shared comparison of A*, Dijkstra, RRT, RRT*, and PRM
• Interactive shared-goal selection
• Planning search animation and final path comparison
• Five simultaneous autonomous robot simulations
• Dynamic Window Approach local control and obstacle avoidance
• Simulated 360-degree LiDAR
• Independent live occupancy maps
• CSV, MAT, PNG, and AVI result export
• Fixed random seed for repeatable environment generation
• Example planning and navigation metrics included
• H.264 MP4 demonstration videos included

Navigation and Mapping

• Differential-drive robot model
• Path following and terminal goal control
• Collision checking and safety-inflated occupancy grid
• Odometry prediction
• Correlative scan matching
• Log-odds occupancy updates
• Optional simulator-reference pose anchoring for limiting map-frame drift

Included planners

• A*
• Dijkstra
• RRT
• RRT*
• PRM

Environment

• MATLAB
• No third-party packages required
• Desktop session required for interactive figures and animations

Notes

The included performance metrics represent one example environment and one selected goal. They should not be interpreted as statistical evidence that one planning algorithm is generally superior.