This　paper　proposes　a　novel　robust　differential　game　scheme　to　solve　the　collision　avoidance　problem　for　networked　multi-agent　systems　(MASs),　subject　to　linear　dynamics,　external　disturbances　and　limited　observation　capabilities.　Compared　with　the　existing　differential　game　approaches　only　considering　obstacle　avoidance　objectives,　we　explicitly　incorporate　the　trajectory　optimization　target　by　penalizing　the　deviation　from　reference　trajectories,　based　on　the　artificial　potential　field　(APF)　concept.　It　is　proved　that　the　strategies　of　each　agent　defined　by　individual　optimization　problems　will　converge　to　a　local　robust　Nash　equilibrium　(R-NE),　which　further,　with　a　fixed　strong　connection　topology,　will　converge　to　the　global　R-NE.　Additionally,　to　cope　with　the　limited　observation　for　MASs,　local　robust　feedback　control　strategies　are　constructed　based　on　the　best　approximate　cost　function　and　distributed　robust　Hamilton–Jacobi–Isaacs　(DR-HJI)　equations,　which　does　not　require　global　information　of　agents　as　in　the　traditional　Riccati　equation　form.　The　feedback　gains　of　the　control　strategies　are　found　via　the　ant　colony　optimization　(ACO)　algorithm　with　a　non-dominant　sorting　structure　with　convergence　guarantees.　Finally,　simulation　results　are　provided　to　verify　the　efficacy　and　robustness　of　the　novel　scheme.　The　agents　arrived　at　the　targeted　position　collision-free　with　a　reduced　arrival　time,　and　reached　the　targeted　positions　under　disturbance.　©　2022　ISA