To　realize　path　planning　in　complicated　environments,　a　new　immune　network　algorithm　for　path　planning　is　presented.　Inspired　by　the　mechanism　of　idiotypic　network　hypothesis,　an　immune　network　is　constructed　with　the　stimulation　and　suppression　between　the　antigen　and　antibody　by　taking　the　environment　and　robot　behavior　as　antigen　and　antibody　respectively.　To　further　improve　the　searching　capability　of　proposed　algorithm,　an　updating　operator　for　antibody　vitality　is　provided　according　to　Baldwin　effect,　and　the　attenuation　coefficient　of　antibody　vitality　is　adaptively　adjusted.　The　simulation　results　show　that　the　proposed　algorithm　is　characterized　by　self-organizing　and　self-learning,　and　the　convergence　performance　and　planning　capacity　are　remarkably　improved.