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

Liwei Yang1, Lixia Fu1, Ping Li1

  • 1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.

Mathematical Biosciences and Engineering : MBE
|December 14, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient leader-follower ant colony optimization (LF-ACO) for multi-robot path planning. The method enhances path smoothness, convergence speed, and formation control for collaborative robotics tasks.

Keywords:
dynamic tangent point methodformation path planningleader follower-ant colony algorithm (LF-ACO)multi-robot

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multi-robot path planning presents complex challenges including obstacle avoidance and inter-robot collaboration.
  • Existing methods often struggle with efficient and smooth path generation for coordinated multi-robot systems.

Purpose of the Study:

  • To propose an efficient leader-follower ant colony optimization (LF-ACO) algorithm for solving the collaborative path planning problem.
  • To enhance the convergence speed and initial path smoothness using a novel multi-factor heuristic function.
  • To improve the path quality and formation control in grid environments.

Main Methods:

  • Developed a novel Multi-factor heuristic functor incorporating distance and smoothing factors.
  • Reconstructed a leader-follower structure to address multi-robot position constraints.
  • Integrated leader and follower ant pheromones into the ant colony optimization (ACO) update rule.
  • Employed a max-min ant strategy for improved global search capability.
  • Utilized turning point optimization and dynamic cut-point methods for path refinement.

Main Results:

  • The proposed LF-ACO algorithm demonstrated improved convergence speed and initial path smoothness.
  • Enhanced search quality for formation path planning was achieved through integrated pheromone updates.
  • The method successfully addressed position constraints in grid environments.
  • Simulation and experimental results validated the effectiveness in solving path planning and formation problems.

Conclusions:

  • The LF-ACO algorithm provides an efficient solution for multi-robot collaborative path planning.
  • The novel heuristic functions and leader-follower structure significantly improve path quality and convergence.
  • The method is robust and effective, as confirmed by simulations and experiments in MATLAB and ROS.