Mission Planning on Autonomous Avoidance for Spacecraft Confronting Orbital Debris

This paper investigates the mission planning problem for spacecraft confronting orbital debris to achieve autonomous avoidance. Firstly, combined with the avoidance requirements, a closed-loop framework of autonomous avoidance for orbital debris is proposed. Under the established model of mission planning, a two-stage planning is proposed to coordinate the conflict between routine tasks and debris avoidance. During the planning for expansion, the temporal constraints for duration actions are handled by the ordering choices. Meanwhile, dynamic resource variables satisfying instantaneous numerical change and continuous linear change are reasoned in the execution of actions. Linear Programming (LP) can solve the bounds of variables in each state, which is used to check the consistency of the interactive constraints on duration and resource. Then, the temporal relaxed planning graph (TRPG) heuristics is rationally developed to guide the plan towards the goal. Finally, the simulation demonstrates that the proposed mission planning strategy can effectively achieve the autonomous debris avoidance of the spacecraft.

Multi-View Clustering from the Perspective of Mutual Information

Exploring the complementary information of multi-view data to improve clustering effects is a crucial issue in multi-view clustering. In this paper, we propose a novel model based on information theory termed Informative Multi-View Clustering (IMVC), which extracts the common and view-specific information hidden in multi-view data and constructs a clustering-oriented comprehensive representation. More specifically, we concatenate multiple features into a unified feature representation, then pass it through a encoder to retrieve the common representation across views. Simultaneously, the features of each view are sent to a encoder to produce a compact view-specific representation, respectively. Thus, we constrain the mutual information between the common representation and view-specific representations to be minimal for obtaining multi-level information. Further, the common representation and view-specific representation are spliced to model the refined representation of each view, which is fed into a decoder to reconstruct the initial data with maximizing their mutual information. In order to form a comprehensive representation, the common representation and all view-specific representations are concatenated. Furthermore, to accommodate the comprehensive representation better for the clustering task, we maximize the mutual information between an instance and its k-nearest neighbors to enhance the intra-cluster aggregation, thus inducing well separation of different clusters at the overall aspect. Finally, we conduct extensive experiments on six benchmark datasets, and the experimental results indicate that the proposed IMVC outperforms other methods.