Conflict-free and energy-efficient path planning for multi-robots based on priority free ant colony optimization

Area of Science:

• Robotics
• Artificial Intelligence
• Optimization Algorithms

Background:

• Robots face limitations in energy storage and complex pathfinding challenges.
• Multi-agent path finding (MAPF) suffers from high coupling issues, especially in unstructured environments.

Purpose of the Study:

• To develop a priority-free ant colony optimization (PFACO) for energy-efficient and conflict-free path planning for multi-robot systems.
• To reduce the motion cost of multiple robots operating in rough terrain.

Main Methods:

• A dual-resolution grid map was designed to model rough terrain, incorporating obstacles and ground friction.
• An energy-constrained ant colony optimization (ECACO) was proposed for single-robot energy-optimal path planning, enhancing the heuristic function and pheromone update strategy.
• Prioritized Conflict-Free Strategy (PCS) and Route Conflict-Free Strategy (RCS) were integrated into ECACO for multi-robot conflict-free planning.

Main Results:

• ECACO demonstrated superior energy savings for single-robot motion across different neighborhood search strategies.
• PFACO successfully generated conflict-free and energy-saving paths for robots in complex scenarios.
• The proposed methods show practical value for real-world multi-robot applications.

Conclusions:

• The developed PFACO effectively addresses the challenges of MAPF in rough environments.
• The approach offers a significant reduction in multi-robot motion costs and improved energy efficiency.
• This research provides a valuable reference for solving practical multi-robot path planning problems.