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Aiming at the optimization path problem of handling robot in the obstacle environment, an novel ant colony algorithm for path planning based on artificial immune algorithm (AI-ACA) is presented. The planning result of ant colony algorithm (ACA) depends on the selection of key parameters,such as pheromone volatilization coefficients, expected arouse factor and information arouse factor. The parameters of traditional ACA are set through experience. But optimal parameter values of path optimization vary remarkably in different obstacle environments. To solve the effect of experiential parameters on the path planning results in different environments, artificial immune algorithm (AIA) is introduced, and the algorithm is used to achieve the iterative optimization of associated parameters of ant colony algorithm and then to improve path optimizing result of ant colony algorithm. Simulation results and test results on self-made robot platform show AI-ACA can be used to effectively make path planning in different obstacle environments. AI-ACA is superior to invariable parameter ant colony path planning referred in the same environment and can raise transportation efficiency of the whole system.