In　order　to　improve　the　intelligent　equipment　level　of　bioremediation　technology,　a　heavily　polluted　coke　plant　was　taken　as　the　research　environment,　and　the　double　deep　Q　network　(DDQN)　and　ant　colony　optimization　algorithm　(ACO)　were　used　to　establish　a　multiple　unmanned　ground　vehicles　(multi-UGV)　path　planning　and　task　assignment　system　for　the　topographical　features　of　the　coke　plant　to　achieve　safe　and　accurate　transportation　of　contaminated　soil　in　the　soil　remediation　process　and　improve　the　efficiency　of　contaminated　soil　transportation.　The　results　showed　that　the　multi-UGV　transportation　system　based　on　DDQN　and　ACO　had　good　path　planning　capability,　and　the　ACO　task　assignment　algorithm　based　on　the　actual　system　time　cost　could　achieve　a　stable　reduction　of　UGV　system　time　cost　under　different　loading　quantities　compared　with　other　task　assignment　strategies　obtained　based　on　simple　linear　distance　or　based　on　the　greedy　algorithm.　©　Journal　of　Central　South　University　(Medical　Science).　All　rights　reserved.