Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm.

Area of Science:

• Robotics and Artificial Intelligence
• Optimization Algorithms
• Computational Intelligence

Background:

• Traditional Sparrow Search Algorithm (SSA) suffers from high time consumption, long path length, and poor obstacle avoidance (static and dynamic).
• Premature convergence and local optima hinder SSA's effectiveness in complex path planning scenarios.

Purpose of the Study:

• To address the limitations of the traditional SSA in path planning.
• To develop a novel algorithm for efficient and safe path planning in dynamic environments.

Main Methods:

• Proposed an improved SSA (ISSA-DWA) incorporating Cauchy reverse learning for population initialization.
• Integrated sine-cosine algorithm for producer position updates to balance global search and local exploration.
• Employed Lévy flight strategy for scrounger position updates to prevent local optima.
• Combined the improved SSA with the Dynamic Window Approach (DWA) for enhanced local obstacle avoidance.

Main Results:

• ISSA-DWA reduced path length by 13.42%, path turning times by 63.02%, and execution time by 51.35% compared to traditional SSA.
• Path smoothness was improved by 62.29% using the ISSA-DWA.
• Demonstrated effective avoidance of both static and dynamic obstacles in complex environments.

Conclusions:

• The proposed ISSA-DWA effectively overcomes the shortcomings of the traditional SSA.
• ISSA-DWA offers a safe, efficient, and highly smooth path planning solution for dynamic obstacle environments.