Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services. Although meshes for electrostatic interaction computations can be reconstructed from point clouds, they are usually too dense, leading to high computational costs. This paper presents an optimization method for converting dense meshes into optimal meshes, enabling fast and accurate computation of the electrostatic interaction by point clouds. First, the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators. Second, the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique, ultimately yielding an optimal mesh for computing the electrostatic interaction. Simulation results show that our method effectively optimizes dense meshes, making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.

Inertial characteristics of non-cooperative targets are crucial for space capture and subsequent on-orbit servicing. Previous methods for identifying inertial parameters involve proximity operations, which are associated with the risk of collision with non-cooperative targets. This paper introduces a long-range, contactless method for identifying the inertial parameters of a non-cooperative target during the pre-capture phase. Specifically, electrostatic interaction is used as an external excitation to alter the target’s motion. A force estimation algorithm that uses measurements from visual and potential sensors is proposed to estimate the electrostatic interaction and eliminate the need for force sensors. Furthermore, a recursive estimation–identification framework is presented to concurrently estimate the coupled motion state, weak electrostatic interaction, and inertial parameters of the target. The simulation results show that the proposed method extends the identification distance to 170 times that of the previous method while maintaining high identification precision for all parameters.