The　existing　path　planning　algorithms　seldom　consider　the　problem　of　security,　and　the　traditional　proximal　policy　optimization(PPO)　algorithm　has　a　variance　adaptability　problem.　To　solve　these　problems,　the　Safe-PPO　algorithm　combining　evolutionary　strategy　and　safety　reward　function　was　proposed.　The　algorithm　is　safety-oriented　for　path　planning.　CMA-ES　was　used　to　improve　the　PPO　algorithm.　The　hazard　coefficient　and　movement　coefficient　were　introduced　to　evaluate　the　safety　of　the　path.　Used　a　grid　map　for　simulation　experiments,　and　compared　the　traditional　PPO　algorithm　with　the　Safe-PPO　algorithm;　The　hexapod　robot　was　used　to　carry　out　the　physical　experiment　in　the　constructed　scene.　The　simulation　results　show　that　the　Safe-PPO　algorithm　is　reasonable　and　feasible　in　safety-oriented　path　planning.　When　compared　to　the　conventional　PPO　algorithm,　the　Safe-PPO　algorithm　increased　the　rate　of　convergence　during　training　by　18%　and　the　incentive　received　by　5.3%.　Using　the　algorithm　that　combined　the　Hazard　coefficient　and　movement　coefficient　during　testing　enabled　the　robot　to　learn　to　choose　the　safer　path　rather　than　the　fastest　one.　The　outcomes　of　the　physical　testing　demonstrated　that　the　robot　could　select　a　more　secure　route　to　the　objective　in　the　created　setting.　©　2023　Beijing　University　of　Aeronautics　and　Astronautics　(BUAA).　All　rights　reserved.