When　a　rapidly-exploring　random　tree　(RRT)　algorithm　is　used　for　path　planning　in　a　complicated　environment　with　many　random　barriers,　the　convergence　is　slow　and　the　obtained　path　is　usually　twisted.　To　meet　the　requirements　of　path　planning　of　an　intelligent　vehicle　in　a　complicated　environment,　a　motion　planning　algorithm,　named　homocentric　circles　RRT　algorithm,　based　on　a　fast　searching　random　tree　is　proposed.　Based　on　basic　RRT　and　combined　with　the　kinematic　constraints　of　the　intelligent　vehicle,　the　homocentric　circles　sampling　strategy　and　adjacent　point　selection　method　were　introduced　in　the　proposed　algorithm.　The　homocentric　circles　sampling　considers　the　target　point　as　the　center;　the　homocentric　circles　coefficient　m　was　used　to　adjust　the　density　of　the　homocentric　circles　to　generate　random　points　to　determine　the　next　path　point.　Considering　the　vehicle　kinematic　constraints　and　target　distance　factor,　the　adjacent　point　selection　method　was　adopted　to　calculate　the　proximity　coefficient,　and　the　random　tree　node　corresponding　to　the　minimum　proximity　coefficient　was　taken　as　the　adjacent　point.　For　the　planned　path,　a　path　processing　method　based　on　vehicle　kinematic　constraints　was　used　to　simplify　the　obtained　path,　and　the　cubic　B-spline　curve　was　employed　to　optimize　the　path　to　generate　a　smooth　and　executable　path.　The　results　show　that　the　algorithm　takes　the　least　time　to　find　the　path　when　the　coefficient　of　homocentric　circles　is　in　the　range　of　0.5-1.5.　A　larger　constraint　value　for　the　angle　of　vehicle　attitude　and　next　path　point　implies　that　less　time　is　used　to　find　the　path　and　it　tends　to　be　stable　when　the　angle　is　35°.　Under　the　same　environment,　the　quality　of　the　planned　path　obtained　using　the　proposed　RRT　improves　considerably　compared　with　the　basic　RRT,　target　bias　RRT,　and　updated　RRT.　Compared　with　the　RRT,　target　bias　RRT,　and　updated　RRT　algorithms,　the　required　time　and　length　of　the　planned　path　of　the　proposed　RRT　algorithm　are　lower　by　43.1%　and　18.7%,　7.3%　and　15.5%,　and　29.6%　and　7%　respectively.　Finally,　the　effectiveness　and　practicability　of　the　algorithm　were　verified　through　the　intelligent　vehicle　experiment.　©　2021,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.