Task Planning for Multiple-Satellite Space-Situational-Awareness Systems
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 superv...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/8/3/73 |
| _version_ | 1797541719215964160 |
|---|---|
| author | Yutao Chen Guoqing Tian Junyou Guo Jie Huang |
| author_facet | Yutao Chen Guoqing Tian Junyou Guo Jie Huang |
| author_sort | Yutao Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:19:44Z |
| format | Article |
| id | doaj.art-d95f5adaf7914487be01795fccd39f20 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T13:19:44Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-d95f5adaf7914487be01795fccd39f202023-11-21T10:10:38ZengMDPI AGAerospace2226-43102021-03-01837310.3390/aerospace8030073Task Planning for Multiple-Satellite Space-Situational-Awareness SystemsYutao Chen0Guoqing Tian1Junyou Guo2Jie Huang3College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaSpace 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.https://www.mdpi.com/2226-4310/8/3/73space situational awarenesstask planningcontract-net protocolmultiagent system |
| spellingShingle | Yutao Chen Guoqing Tian Junyou Guo Jie Huang Task Planning for Multiple-Satellite Space-Situational-Awareness Systems Aerospace space situational awareness task planning contract-net protocol multiagent system |
| title | Task Planning for Multiple-Satellite Space-Situational-Awareness Systems |
| title_full | Task Planning for Multiple-Satellite Space-Situational-Awareness Systems |
| title_fullStr | Task Planning for Multiple-Satellite Space-Situational-Awareness Systems |
| title_full_unstemmed | Task Planning for Multiple-Satellite Space-Situational-Awareness Systems |
| title_short | Task Planning for Multiple-Satellite Space-Situational-Awareness Systems |
| title_sort | task planning for multiple satellite space situational awareness systems |
| topic | space situational awareness task planning contract-net protocol multiagent system |
| url | https://www.mdpi.com/2226-4310/8/3/73 |
| work_keys_str_mv | AT yutaochen taskplanningformultiplesatellitespacesituationalawarenesssystems AT guoqingtian taskplanningformultiplesatellitespacesituationalawarenesssystems AT junyouguo taskplanningformultiplesatellitespacesituationalawarenesssystems AT jiehuang taskplanningformultiplesatellitespacesituationalawarenesssystems |