This paper presents a vision-guided nonlinear model predictive control (MPC) framework for coordinated dual-manipulator pre-contact acquisition of tumbling targets. Temporal monocular estimation supplies pose, motion, and uncertainty; rigid-body prediction generates fixture trajectories; and model-based or diffusion candidates initialize exact Denavit–Hartenberg (DH) refinement before clearance-checked execution. Synthetic tests give mean position and attitude errors of 0.0170–0.0182 m and 1.05–1.19°. With initial-state uncertainty and $\pm 6\%$ inertia mismatch, 3 s fixture-prediction root-mean-square error is 0.0822 m with 94.5% covariance coverage. In 30 paired stress scenarios, original-criterion safe capture is 19/30 for exact-DH MPC versus 14/30 for linearized MPC (exact McNemar $p=0.180$). A separate reviewer-motivated post hoc sensitivity imposes three-step angular-rate matching uniformly across five methods: safe capture is 19/30 versus 8/30 (Holm-adjusted $p=0.0171$). Diffusion and uncertainty inflation add no safe captures. Actual-cycle analysis finds a small 1.15% iteration reduction with diffusion, without an end-to-end timing benefit. Sequential mean update times are 1.234 s without diffusion and 1.392 s with diffusion, exceeding the 0.10 s simulated update interval. The evidence concerns delay-free, fixed-base kinematic supervisory planning with target-specific synthetic vision, not real-time hardware control or flight readiness.

Accurate attitude measurement of non-cooperative space targets is essential for on-orbit servicing, active debris removal, and autonomous rendezvous missions. To address the challenges associated with unknown geometry, rapid tumbling motion, and the limited observability of single-view systems, this study proposes a cooperative multi-view measurement framework for three-dimensional reconstruction and attitude estimation. Multiple spacecraft are deployed to form a stable observation configuration, and multi-view image sequences are acquired to strengthen geometric constraints. A learning-based multi-view stereo reconstruction module is used to estimate depth information and reconstruct point clouds, which are further processed through iterative closest point (ICP) registration to derive inter-frame attitude variations. An extended Kalman filter (EKF) is then introduced to improve temporal consistency and suppress measurement noise. Validation is conducted in a numerical simulation using a simplified Fengyun-1 (FY-1) satellite model under a three-spacecraft cooperative fly-around scenario. The simulation results demonstrate that the proposed method achieves high-precision attitude estimation, with attitude errors below $0.3°$ and positional errors within $0.05\mathrm{m}$. Comparative experiments show that the method maintains stable measurement performance under varying observation distances and viewing configurations. The proposed framework provides a reliable and robust measurement solution for dynamic attitude determination of free-tumbling space targets.

The contactless electromagnetic detumbling method is a safe and reliable on-orbit detumbling technique for space tumbling targets. However, its efficiency substantially decreases as the relative angular velocity of the target reduces. To address this issue, this paper proposes an optimal electromagnetic detumbling method for space tumbling targets based on multisatellite collaboration. First, the influence of the relative position and orientation between a servicing satellite and the tumbling target on detumbling efficiency is analyzed, and an optimal method for generating the desired detumbling magnetic field through multisatellite formation design is proposed. Furthermore, to address the reduction in eddy current torque caused by the decay of the target’s relative angular velocity during detumbling, optimal reconstruction criteria for multisatellite formations are established. By dynamically adjusting the relative positions and orientations of the servicing satellites, the detumbling efficiency is continuously improved. Numerical simulations using a formation of 5 servicing satellites demonstrate the effectiveness of the proposed method. Results show that compared to traditional single-satellite detumbling methods, the proposed method improves detumbling efficiency by 4.68 times and markedly reduces the detumbling time.