Non-cooperative　target　herding　is　one　of　the　concerns　in　the　robotics　field.　For　the　non-cooperative　target　herding　problem,　a　general　planning　framework　is　proposed　to　compel　the　target　to　the　destination　by　a　mobile　robot　team　with　pursuit,　encirclement,　and　guidance　operations.　In　the　proposed　planning　framework,　the　encirclement　strategy　and　guidance　strategy　are　designed　to　deal　with　the　unpredictability　of　the　target.　Firstly,　the　mobile　robots　approach　the　randomly　moving　target　in　the　pursuit　process.　Secondly,　the　encirclement　strategy　is　applied　in　the　encirclement　process　to　enable　the　mobile　robot　team　to　encircle　the　non-cooperative　target.　Finally,　the　guidance　strategy　is　employed　in　the　guidance　process　to　enable　the　mobile　robot　team　to　form　a　favorable　encirclement　formation　and　compel　the　non-cooperative　target　to　the　destination.　During　the　herding　task,　the　mobile　robots　encircle　the　target　or　other　mobile　robots　in　a　satellite-like　motion.　Moreover,　two　queues　(orbiters　and　wanders)　are　maintained　to　change　the　formation　adaptively,　improving　the　flexibility　of　the　framework.　Various　environments　are　designed　to　verify　the　effectiveness　of　the　planning　framework　both　in　simulations　and　real-world　experiments.　Furthermore,　the　proposed　framework　is　a　general　platform　having　great　potential　for　various　applications,　where　the　numbers　of　mobile　robots　in　the　herding　team　are　changeable,　and　different　planning　algorithms　can　be　integrated　into　the　framework.　Note　to　Practitioners　-　The　motivation　for　this　paper　is　to　propose　a　planning　framework　to　efficiently　herd　the　non-cooperative　target　in　various　scenarios　for　applications　such　as　the　maintenance　of　public　safety　and　the　facilitation　of　wildlife　migration.　The　existing　cooperative　herding　methods　typically　maintain　relatively　fixed　formations,　while　the　planning　framework　proposed　in　this　paper　introduces　a　satellite-based　motion　pattern　and　diverse　formation　transformation　strategies,　thereby　enhancing　formation　flexibility　and　adaptability.　Consequently,　this　framework　can　be　effectively　applied　to　challenging　environments　such　as　those　with　restricted　areas　and　dynamic　obstacles.　Furthermore,　the　proposed　planning　framework　allows　for　flexible　adjustments　in　path　planning　methods　and　the　number　of　mobile　robots　to　meet　various　practical　application　requirements.　Through　a　series　of　simulations　and　real-world　experiments,　the　reliability　and　favorable　performance　of　the　proposed　algorithm　framework　in　practical　applications　are　demonstrated.　©　2004-2012　IEEE.