Rapid Sequence Generation for Active Debris Removal Mission Based on Attention Mechanism and Pointer Network

This paper proposes a method based on the attention mechanism combined with a pointer network for rapidly generating sequences of Active debris removal (ADR) missions. Initially, the paper introduces the problem and modeling of ADR missions, establishing the multi-target planning problem as a time-dependent traveling salesman problem (TDTSP) considering the time dimension. Subsequently, during the spacecraft transfer process, it considers using continuous-thrust transfer, utilizing Pontryagin’s minimum principle to transform the fuel-optimal continuous-thrust transfer problem into a two-point boundary value problem (TPBVP), then rapidly generating a continuous-thrust dataset to train a surrogate model for evaluating continuous-thrust cost. Furthermore, PN-attention is proposed, which is derived from a pointer network (PN) model that incorporates attention computation, aimed at learning the sequence planning of ADR problems. In the validation and discussion section, PN-attention’s performance in solving ADR problems is compared with various heuristic algorithms, verifying its effectiveness, optimality, generalizability, and solution speed.