LF-ACO: an effective formation path planning for multi-mobile robot

Area of Science:

• Robotics
• Artificial Intelligence
• Optimization Algorithms

Background:

• Multi-robot path planning presents complex challenges, including obstacle avoidance and inter-robot collaboration, distinguishing it from single-robot scenarios.
• Existing methods often struggle with efficient collaborative path generation and maintaining formations in dynamic environments.

Purpose of the Study:

• To propose an efficient Leader-Follower Ant Colony Optimization (LF-ACO) algorithm for solving the collaborative path planning problem in multi-robot systems.
• To enhance the convergence speed and initial path smoothness using a novel Multi-factor heuristic functor.
• To improve the search quality and global exploration capabilities for multi-robot formation path planning.

Main Methods:

• Developed a Multi-factor heuristic functor incorporating distance and smoothing factors to improve initial path characteristics.
• Reconstructed a leader-follower structure to address position constraints for multi-robots in grid environments.
• Integrated leader and follower ant pheromones into the Ant Colony Optimization (ACO) pheromone update rule.
• Employed a max-min ant strategy to boost global search capabilities.
• Utilized turning point optimization and dynamic cut-point methods for further path refinement.

Main Results:

• The proposed LF-ACO algorithm demonstrated improved convergence speed and enhanced initial path smoothness.
• The leader-follower structure effectively managed position constraints in grid environments.
• Pheromone integration and max-min strategy improved the quality and efficiency of formation path searches.
• Post-processing optimization techniques further enhanced the overall path quality.

Conclusions:

• The LF-ACO method successfully addresses multi-robot path planning and formation challenges.
• Simulation and experimental results validate the algorithm's effectiveness in practical robotics applications.
• The approach offers a robust solution for complex collaborative robotics tasks requiring efficient path generation.