When　performing　unknown　target　collection　tasks,　mobile　robots　frequently　face　challenges　such　as　unknown　environments,　lack　of　information　about　targets.　These　challenges　can　lead　robots　to　overlook　corner　regions　and　expand　coverage,　resulting　in　low　task　completion　efficiency　and　long　redundant　paths.　To　address　these　issues,　a　simultaneous　exploration　and　coverage　path　planning　(SECPP)　algorithm　is　proposed.　First,　an　information　gain　function　consisting　of　the　surrounding　environment　information　and　the　movement　cost　is　designed.　Candidate　exploration　points　are　generated　by　sampling　the　frontier　points,　and　the　point　with　the　maximum　information　gain　is　considered　as　the　actual　exploration　point.　Then,　a　balancing　framework　is　established　to　evaluate　the　surrounding　environment　based　on　status　changes　in　the　environment.　If　the　local　environment　remains　unexplored,　the　robot　continues　the　exploration　task　based　on　the　selected　exploration　point.　If　the　local　environment　is　explored,　the　algorithm　extracts　task　region　information　and　designs　a　coverage　reward　function　consisting　of　the　exploration　path　and　guide　point　to　generate　a　coverage　path.　The　robot　follows　the　path　and　collects　all　targets　in　the　region.　Finally,　the　proposed　SECPP　algorithm　is　compared　with　other　advanced　similar　algorithms.　The　results　demonstrate　that　the　SECPP　can　accomplish　the　unknown　target　collection　task　with　a　shorter　repeated　path　length,　fewer　turns,　and　less　time.　©　2025　Northeast　University.　All　rights　reserved.