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Application of Improved Sparrow Search Algorithm to Path Planning of Mobile Robots
Yong Xu1, Bicong Sang1, Yi Zhang1
1College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130119, China.
Biomimetics (Basel, Switzerland)
|June 26, 2024
Summary
An improved sparrow search algorithm (ISSA) enhances mobile robot path planning efficiency and stability. This algorithm offers superior path length, reduced running time, and better optimality compared to standard methods.
Area of Science:
- Robotics
- Artificial Intelligence
- Optimization Algorithms
Background:
- Efficient, stable, and safe path planning is crucial for modern robotics.
- Existing algorithms face challenges in complex and dynamic environments.
Purpose of the Study:
- To introduce an improved sparrow search algorithm (ISSA) for enhanced path planning.
- To improve the efficiency, stability, and optimality of mobile robot path planning.
Main Methods:
- Initialization using circle chaotic mapping for population diversity.
- Incorporation of northern goshawk's location update for broader search.
- Lévy flight strategy for improved global optimization.
- Adaptive T-distribution mutation for enhanced local exploration and convergence speed.
Main Results:
- ISSA demonstrated superior performance over the standard sparrow search algorithm (SSA).
- Improvements noted in path length, running time, path optimality, and stability.
- Validation on the CEC2021 function set and mobile robot path planning scenarios.
Conclusions:
- The proposed ISSA is effective, robust, and feasible for mobile robot path planning.
- ISSA offers significant advantages over existing methods for complex path planning tasks.
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