This paper proposes an improved genetic algorithm (GA) for global path planning of robots in complex environments. Traditional genetic algorithms suffer from problems such as local optima and slow iteration speed. To address these issues, several optimizations are made in this work: First, the traditional 8-direction search is extended to a 24-neighborhood 16-direction search, enhancing the global search capability. A PT chaotic mapping fusion strategy is introduced, where sequences generated by Piecewise chaotic mapping are used as parameters for tent map chaos, which improves the population diversity. Secondly, the Levy flight strategy is integrated to avoid local stagnation, and a new out-of-bounds particle handling strategy is proposed to prevent the initialization population from exceeding boundaries. Next, a pairing exchange and differential perturbation mechanism are designed to prevent the loss of good individuals, thus avoiding convergence to local optima. Lastly, a new pressure-level splitting selection mechanism and an adaptive crossover-mutation probability adjustment operator are proposed to address the issue of excessive selection pressure. A nonlinear exponential function is used to adjust the crossover selection probability to avoid premature divergence, while a complementary adjustment of the mutation probability is employed to expand the search space and reduce convergence oscillations. Experimental results demonstrate that the proposed method significantly improves path planning performance compared to traditional genetic algorithms and other improved algorithms, with path lengths reduced by 5.13% and 2.06%, respectively. This verifies its superiority and practicality in robot path planning.