With　the　development　of　technology,　robots　are　gradually　being　used　more　and　more　widely　in　various　fields.　Industrial　robots　need　to　perform　path　planning　in　the　course　of　their　tasks,　but　there　is　still　a　lack　of　a　simple　and　effective　method　to　implement　path　planning　in　complex　industrial　scenarios.　In　this　paper,　an　improved　whale　optimization　algorithm　is　proposed　to　solve　the　robot　path　planning　problem.　The　algorithm　initially　uses　a　logistic　chaotic　mapping　approach　for　population　initialization　to　enhance　the　initial　population　diversity,　and　proposes　a　jumping　mechanism　to　help　the　population　jump　out　of　the　local　optimum　and　enhance　the　global　search　capability　of　the　population.　The　proposed　algorithm　is　tested　on　12　complex　test　functions　and　the　experimental　results　show　that　the　improved　algorithm　achieves　the　best　results　in　several　test　functions.　The　algorithm　is　then　applied　to　a　path　planning　problem　and　the　results　show　that　the　algorithm　can　help　the　robot　to　perform　correct　and　efficient　path　planning.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.