With　the　development　of　artificial　intelligence,　the　application　of　unmanned　ground　vehicles　(UGV)　in　outdoor　hazardous　scenarios　has　received　more　attention.　However,　the　terrains　in　these　environments　are　often　complex　and　undulating,　which　also　pose　higher　challenges　to　the　multi-UGV　path　planning　and　task　assignment　(MUPPTA)　optimization.　To　efficiently　improve　the　multi-UGV　collaboration　in　3D　environments,　a　MUPPTA　method　is　proposed　based　on　double　deep　Q　learning　network　(DDQN)　and　ant　colony　optimization　(ACO)　to　jointly　optimize　the　path　planning　and　task　assignment　decisions　of　multiple　UGVs.　The　authors　first　comprehensively　consider　the　characteristics　of　the　3D　environments,　and　model　the　MUPPTA　problem　as　a　combinatorial　optimization　problem.　To　tackle　it,　the　original　problem　is　decomposed　into　the　multi-UGV　path　planning　sub-problem　and　task　assignment　sub-problem,　and　solve　them　separately.　First,　the　path　planning　sub-problem　in　the　3D　environments　is　transformed　into　a　Markov　decision　process　(MDP)　model,　and　a　multi-UGV　path　planning　algorithm　based　on　DDQN　(MUPP-DDQN)　is　proposed　to　obtain　the　optimal　paths　and　actual　path　costs　between　tasks　through　extensive　offline　learning　and　training.　Based　on　this,　a　multi-UGV　task　assignment　algorithm　is　further　proposed　based　on　ACO　(MUTA-ACO)　to　solve　the　task　assignment　sub-problem　and　achieve　the　optimal　task　assignment　solution.　Simulation　results　show　that　the　proposed　method　is　more　cost-effective　and　time-saving　compared　to　other　comparison algorithms.　©　2024　The　Author(s).　IET　Intelligent　Transport　Systems　published　by　John　Wiley　&　Sons　Ltd　on　behalf　of　The　Institution　of　Engineering　and　Technology.