Multi-Strategy Enhanced COA for Path Planning in Autonomous Navigation.
Abstract
Autonomous navigation is reshaping various domains in people's life by enabling safe and efficient movement in complex environments. Reliable navigation requires path planning algorithms that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing algorithms 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 strategies: 1) Refractive Learning to enhance diversity and global exploration, 2) Stochastic CentroidGuided Exploration to balance global and local search, and 3) Adaptive Competition-Based Selection to accelerate convergence and improve solution quality. Experimental results show that MCOA significantly improves the performance of 3D UAV path planning, reducing computation time by 69.2% and trajectory cost by 67.0% compared to 11 baseline algorithms, which demonstrates its effectiveness in autonomous navigation within complex environments. Index Terms-Autonomous Navigation, Path Planning, Crayfish Optimization Algorithm, Unmanned Aerial Vehicle I. I NTRODUCTION With rapid advancements in artificial intelligence, sensor technology, and computing power, autonomous navigation has been widely adopted across various domains [1]. It plays a vital role in systems operating in various environments, such as unmanned aerial vehicles (UAVs), mobile robots, and autonomous vehicles, by enabling independent perception, decision-making, and task execution. At its core, path planning is a key process for ensuring safe and efficient movement. It aims to compute an optimal or near-optimal trajectory that meets task-specific constraints while avoiding obstacles, typically through the use of algorithms designed for trajectory optimization [2]. Heuristic and metaheuristic algorithms, such as Genetic Algorithm (GA) [3], Particle Swarm Optimization (PSO) [4], Ant Colony Optimization (ACO) [5], and Crayfish Optimization Algorithm (COA) [6], have proven effective