This　paper　describes　a　nonholonomic　robotic　wheeled　vehicle　ripple　tentacle　motion　planning　method,　aiming　to　improve　the　vehicle＇s　trajectory　smoothness　and　avoid　frequent　weight　parameters　adjustment　in　different　environments.　In　the　regular　tentacle　motion　planning　algorithm,　the　planning　result　is　selected　among　the　drivable　tentacles　using　a　weighted　sum　cost　function.　Though　the　method　is　simple　and　easy　to　understand,　it　is　difficult　to　adjust　the　weighted　coefficients　in　different　environments.　To　solve　this　problem,　a　geometrical　ripple　tentacles　technique　is　used　to　choose　a　tentacle　as　a　sub-optimal　path.　Compared　with　the　regular　tentacles　algorithm,　the　proposed　ripple　tentacle　algorithm　can　get　a　better　performance　in　vehicle＇s　trajectory　smoothness　with　an　acceptable　runtime　expense.　And　another　two　traits　can　also　distinguish　this　method:　(a)　it　can　avoid　weight　parameter　adjustment　in　different　environments　and　varied　vehicle＇s　states,　and　(b)　it　can　be　used　in　both　unknown　environment　and　partly　known　environment　with　goal　point　and　global　reference　path.　In　the　totally　unknown　environment,　it　acts　as　a　pure　obstacle　avoidance　algorithm,　and　when　there　is　a　global　path,　it　can　follow　the　reference　path　and　avoid　hazards　simultaneously.　©　2012　IEEE.