An efficient and global interactive optimization methodology for path planning with multiple routing constraints.

Area of Science:

• Robotics and Control Systems
• Operations Research
• Computer Science

Background:

• Path planning is crucial for autonomous systems and industrial processes.
• Existing algorithms struggle with realistic constraints like forbidden paths or vertices.
• Complex routing constraints limit the efficiency and feasibility of current methods.

Purpose of the Study:

• To develop an efficient path planning methodology for complex scenarios with multiple routing constraints.
• To address the limitations of existing algorithms in handling specified and forbidden vertices/paths.
• To propose a novel approach combining adaptive coding strategies and swarm intelligence for optimal path finding.

Main Methods:

• Transforming complex routing constraints into mathematical expressions.
• Developing an adaptive vertex priority strategy for coding.
• Implementing a global optimization methodology based on swarm intelligence algorithms.
• Theoretically proving the optimal convergence condition of the proposed methodology.

Main Results:

• The proposed methodology effectively handles complex path planning problems with multiple constraints.
• Experiments demonstrate the efficiency and superiority of the new approach compared to existing algorithms.
• The adaptive strategy and swarm intelligence integration enhance pathfinding capabilities.

Conclusions:

• The developed methodology provides a robust solution for complex path planning in autonomous and industrial systems.
• The approach overcomes the limitations of traditional algorithms by effectively managing diverse routing constraints.
• This work contributes to advancing the field of intelligent path planning with proven efficiency and superiority.