Multi-Strategy Enhanced COA for Path Planning in Autonomous Navigation

TL;DR

This work addresses the challenge of slow convergence and suboptimal paths in autonomous navigation by introducing MCOA, a multi-strategy enhancement of the Crayfish Optimization Algorithm. By integrating Refractive Opposition Learning, Stochastic Centroid-Guided Exploration, and Adaptive Competition-Based Selection, MCOA improves population diversity, balances global/local search, and accelerates convergence. Empirical results in 3D UAV and 2D mobile robot path planning show substantial gains over 11 baselines, including a 69.2% reduction in computation time and a 16.7% improvement in total path cost for UAV planning, and up to 75.6% shorter average path lengths in grid-based 2D planning. The findings suggest MCOA as a practical tool for real-time autonomous navigation in complex environments, with code available at the authors' GitHub repository.

Abstract

Autonomous navigation is reshaping various domains in people's life by enabling efficient and safe movement in complex environments. Reliable navigation requires algorithmic approaches that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing methods struggle with slow convergence and suboptimal solutions, particularly in complex environments, limiting their real-world applicability. To address these limitations, this paper presents the Multi-Strategy Enhanced Crayfish Optimization Algorithm (MCOA), a novel approach integrating three key strategies: 1) Refractive Opposition Learning, enhancing population diversity and global exploration, 2) Stochastic Centroid-Guided Exploration, balancing global and local search to prevent premature convergence, and 3) Adaptive Competition-Based Selection, dynamically adjusting selection pressure for faster convergence and improved solution quality. Empirical evaluations underscore the remarkable planning speed and the amazing solution quality of MCOA in both 3D Unmanned Aerial Vehicle (UAV) and 2D mobile robot path planning. Against 11 baseline algorithms, MCOA achieved a 69.2% reduction in computational time and a 16.7% improvement in minimizing overall path cost in 3D UAV scenarios. Furthermore, in 2D path planning, MCOA outperformed baseline approaches by 44% on average, with an impressive 75.6% advantage in the largest 60*60 grid setting. These findings validate MCOA as a powerful tool for optimizing autonomous navigation in complex environments. The source code is available at: https://github.com/coedv-hub/MCOA.

Figures (9)

• Figure 1: Flowchart of MCOA
• Figure 2: Refractive Learning for Population Selection
• Figure 3: Six Random Crayfish Populations In Exploration
• Figure 4: Comparison in Total Flight Cost
• Figure 5: Comparison in Four Types of Costs