In　this　article,　the　trajectory　planning　of　the　two　manipulators　of　the　dual-arm　robot　is　studied　to　approach　the　patient　in　a　complex　environment　with　deep　reinforcement　learning　algorithms.　The　shape　of　the　human　body　and　bed　is　complex　which　may　lead　to　the　collision　between　the　human　and　the　robot.　Because　the　sparse　reward　the　robot　obtains　from　the　environment　may　not　support　the　robot　to　accomplish　the　task,　a　neural　network　is　trained　to　control　the　manipulators　of　the　robot　to　prepare　to　hold　the　patient　up　by　using　a　proximal　policy　optimization　algorithm　with　a　continuous　reward　function.　Firstly,　considering　the　realistic　scene,　the　3D　simulation　environment　is　built　to　conduct　the　research.　Secondly,　inspired　by　the　idea　of　the　artificial　potential　field,　a　new　reward　and　punishment　function　was　proposed　to　help　the　robot　obtain　enough　rewards　to　explore　the　environment.　The　function　is　consisting　of　four　parts　which　include　the　reward　guidance　function,　collision　detection,　obstacle　avoidance　function,　and　time　function.　Where　the　reward　guidance　function　is　used　to　guide　the　robot　to　approach　the　targets　to　hold　the　patient,　the　collision　detection　and　obstacle　avoidance　function　are　complementary　to　each　other　and　are　used　to　avoid　obstacles,　and　the　time　function　is　used　to　reduce　the　number　of　training　episode.　Finally,　after　the　robot　is　trained　to　reach　the　targets,　the　training　results　are　analyzed.　Compared　with　the　DDPG　algorithm,　the　PPO　algorithm　reduces　about　4　million　steps　for　training　to　converge.　Moreover,　compared　with　the　other　reward　and　punishment　functions,　the　function　used　in　this　paper　will　obtain　many　more　rewards　at　the　same　training　time.　Apart　from　that,　it　will　take　much　less　time　to　converge,　and　the　episode　length　will　be　shorter;　so,　the　advantage　of　the　algorithm　used　in　this　paper　is　verified.　©　2022　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.