Abstract
Space situational awareness (SSA) plays an important role in maintaining space advantages. Task planning is one of the key technologies in SSA to allocate multiple tasks to multiple satellites, so that a satellite may be allocated to supervise multiple space objects, and a space object may be supervised by multiple satellites. This paper proposes a hierarchical and distributed task-planning framework for SSA systems with focus on fast and effective task planning customized for SSA. In the framework, a global task-planner layer performs satellite and object clustering, so that satellites are clustered into multiple unique clusters on the basis of their positions, while objects are clustered into multiple possibly intersecting clusters, hence allowing for a single object to be supervised by multiple satellites. In each satellite cluster, a local task planner performs distributed task planning using the contract-net protocol (CNP) on the basis of the position and velocity of satellites and objects. In addition, a customized discrete particle swarm optimization (DPSO) algorithm was developed to search for the optimal task-planning result in the CNP. Simulation results showed that the proposed framework can effectively achieve task planning among multiple satellites and space objects. The efficiency and scalability of the proposed framework are demonstrated through static and dynamic orbital simulations.
Highlights
Space situational awareness (SSA) describes the knowledge and understanding of the situation in near-Earth space, including energy, particles, and natural and artificial objects [1,2]
Results showed that the proposed planning framework is capable of generating multiple-to-multiple task-planning patterns, and is significantly more computational efficient when compared to centralized contract-net protocol (CNP) methods
Resource, communication, and distance constraints were not considered when minimizing (3). The inclusion of these constraints certainly increases the complexity of the problem, which is usually solved by computationally demanding algorithms such as mixed-integer [10] and swarm intelligence optimization [18]
Summary
Space situational awareness (SSA) describes the knowledge and understanding of the situation in near-Earth space, including energy, particles, and natural and artificial objects [1,2]. As the main purpose of SSA is to detect and supervise multiple spacecraft, stations, and millions of space debris objects, one of the key technologies for MSS is task planning, i.e., how to allocate multiple tasks to multiple satellites to achieve the best overall execution performance [5]. In Earth observation applications, tasks are usually defined as observing a fixed ground station or a certain ground area within a time window [11,16,24], while SSA applications deal with dynamic objects in near-Earth space. Results showed that the proposed planning framework is capable of generating multiple-to-multiple task-planning patterns, and is significantly more computational efficient when compared to centralized CNP methods. The consideration of both relative distance and velocity during planning reduces the overall distance among satellites and objects in consecutive sampling instants
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