KA-IHO: A Kinematic-Aware Improved Hippo Optimization Algorithm for Collision-Free Mobile Robot Path Planning in...

Area of Science:

• Robotics
• Artificial Intelligence
• Optimization Algorithms

Background:

• Autonomous path planning in obstacle-dense environments is a significant challenge for swarm intelligence.
• Existing methods often suffer from infeasible initialization, poor exploration-exploitation balance, and jerky trajectories.

Purpose of the Study:

• To propose a novel algorithm, the Kinematic-Aware Improved Hippo Optimization (KA-IHO), for mobile robot path planning.
• To address the limitations of existing swarm intelligence methods in complex environments.

Main Methods:

• KA-IHO integrates an elite safety pool initialization, a hierarchical elite-scout update mechanism, and anti-stagnation strategies.
• A Laplacian Line-of-Sight Ironing Operator is used for path smoothing.
• Comparative experiments were conducted on various grid maps against six other algorithms.

Main Results:

• KA-IHO consistently achieved collision-free path planning.
• The algorithm obtained lower mean fitness values and smaller standard deviations, indicating superior robustness and solution quality.
• Hardware experiments confirmed reliable real-world execution with trajectory tracking errors within ±4 cm.

Conclusions:

• The proposed KA-IHO algorithm effectively addresses key challenges in autonomous path planning for mobile robots.
• KA-IHO demonstrates improved performance in terms of feasibility, robustness, and trajectory smoothness compared to existing methods.
• The algorithm's practical applicability is validated through real-robot experiments.