Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Mobile robot navigation in dynamic environments presents significant challenges.
• Efficient and reliable path planning is crucial for industrial and domestic applications.
• Existing methods often struggle with real-time obstacle avoidance and local minima.

Purpose of the Study:

• To propose a real-time dynamic path planning method for mobile robots.
• To develop a system capable of avoiding both static and dynamic obstacles.
• To enhance path planning efficiency and reduce computation time.

Main Methods:

• A hybrid algorithm combining the Beetle Antennae Search (BAS) and Artificial Potential Field (APF) methods, termed BAS-APF.
• Establishing a potential field to accelerate convergence and mitigate local minima issues.
• Implementing a security scope to ensure paths are practical for real-world environments.

Main Results:

• The BAS-APF method demonstrates superior path planning performance compared to traditional approaches.
• The algorithm significantly reduces planning time while ensuring effective obstacle avoidance.
• Simulative results validate the effectiveness and superiority of the proposed method.

Conclusions:

• The proposed BAS-APF method offers an effective solution for real-time dynamic path planning in mobile robotics.
• This approach enhances navigation safety and efficiency in complex, changing environments.
• The method provides a robust and faster alternative for mobile robot navigation systems.