A Hybrid Local Replanning Strategy for Multi-Satellite Imaging Mission Planning in Uncertain Environments

Abstract

Multi-satellite imaging mission planning (MSIMP) research has advanced substantially in recent years. However, contemporary MSIMP research in uncertain environments is still confronting challenges such as loss of satellite resource allocation, inadequate anti-jamming ability of the mission planning scheme, and low mission completion rate. Therefore, in this work, we propose a hybrid local replanning strategy improved adaptive differential evolutionary (HLRS-MSFADE) algorithm based on the multi-satellite imaging mission planning in uncertain environments (MSIMPUE). First, an MSIMPUE model based on uncertainty assessment is constructed. This model solves the problem of assessing new tasks with varied qualities to decide the observation order in an uncertain environment and decreases the loss caused by inefficient satellite resource allocation. Second, to address the issue of difficulty in planning for changing new task requirements in uncertain environments, an HLRS for uncertain environments is developed to ensure efficient task insertion while avoiding conflict costs. Finally, an MSFADE algorithm is presented to handle the problem of long MSIMPUE mission response time and low mission completion rate with good quality in an acceptable computation time. The simulation results validated the effectiveness and stability of the method in dealing with MSIMPUE. Moreover, the HLRS-MSFADE algorithm outperforms previous methods in terms of mission response time, mission completion rate, and motion perturbation.

Keywords

• Uncertain environment
• multi-satellite imaging mission planning
• replanning strategy
• adaptive differential evolutionary algorithm