To address the issues of local optima and slow convergence speed encountered by traditional genetic algorithms, several improvements have been made to the genetic algorithm in this paper. Firstly, the conventional 8-direction search is extended to a 24-neighborhood, 16-direction search to enhance the global search ability. Secondly, a Piecewise and Tent (PT) chaotic mapping fusion strategy is introduced, where the sequence generated by the Piecewise chaotic mapping is used as the parameter for the Tent chaotic mapping to improve population diversity. Furthermore, the Levy flight strategy is integrated to avoid local stagnation, and a new strategy for handling out-of-bounds particles is proposed to prevent the initialization population from exceeding boundaries. A novel pairing exchange and differential perturbation mechanism is then designed to prevent the loss of good individuals, which may lead to the algorithm getting stuck in local optima. Lastly, a new pressure level splitting selection mechanism and an adaptive crossover and mutation probability adjustment operator are proposed. Coefficients are adjusted to resolve the issue of excessive selection pressure, and a nonlinear exponential function is used to adjust the crossover selection probability to avoid early divergence. Additionally, complementary adjustments to mutation probabilities are introduced to expand the search space and reduce convergence oscillations. Experimental results show 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. The superiority and practicality of the method in robot path planning are validated.