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Traditional optimization-based methods often suffer from limited adaptability, premature convergence, or insufficient obstacle handling. To address these challenges, we propose a novel hybrid path planning framework that integrates the Ant Colony Optimizer (ACO), the Whale Optimizer (WOA), the Artificial Potential Field (APF), and a random jump mechanism. This hybrid integration is characterized by the complementary global exploration, local optimization, and smooth trajectory generation, and is supported by a relativistic potential model and quantum-inspired mutation. Experimental results in various unstructured scenarios show that our method achieves significant performance improvements over six commonly used benchmarks including genetic algorithms (GA), genetically engineered word optimization (GWO), and quantum-inspired mutation (IRRT), with a 28.3% reduction in collision frequency and a 19.6% improvement in average path smoothness after preliminary simple quantification. 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