Heuristic Planning for Space Missions

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4952

Special Issue Editors


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Guest Editor
Institute of Deep Space Exploration, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 10081, China
Interests: AI planning; multi-agent system; onboard autonomy of spacecraft; autonomous navigation and control; model identify and reconfigure

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Guest Editor
School of Aerospace Engineering, Beijing Institute of Technology, Beijing 10081, China
Interests: autonomous navigation, guidance, and control for deep space; precise soft landing technology on planetary surfaces
School of Aerospace Engineering, Beijing Institute of Technology, Beijing 10081, China
Interests: autonomous mission planning for spacecraft; spacecraft attitude and trajectory planning; fault detection and diagnosis; intelligent planning system for spacecraft

Special Issue Information

Dear Colleagues, 

Autonomous planning is one of the primary research interests in the field of artificial intelligence. A variety of methods have been proposed to solve classical planning problems, such as the coloring problem and the air cargo transportation problem, including forward state space search, graph planning, hierarchical task network planning, etc. Autonomous planning methods have greatly increased the effectiveness of solving planning problems and promoted the application of planning technology.

In a classical planning task with the search space of transforming an initial world state into a goal-satisfying state, one common means of reaching a solution is to use the heuristic search method. For the candidate nodes in the search space, the heuristic evaluation strategy can provide certain rules with which to calculate the cost of the nodes based on the target set of the planning problem. Thus enables the planner to eliminate the interference of irrelevant nodes during the search process and select the appropriate action or state to speed up the solution of the planning problem. There are two important branches in the study of heuristic evaluation strategies in classical planning: delete relaxation and landmark heuristics.

However, traditional planning methods struggle to deal with spacecraft planning problems that have complex constraints such as time, resources, and other parameters. The space powers have carried out a wealth of studies on autonomous and heuristic planning technology.

Autonomous planning technology was first applied and verified in space exploration missions in the 1990s. In the initial applications, only part of the onboard system operated with autonomy, which reduced the mission operations conducted by ground personnel. The whole-spacecraft autonomy was verified by Deep Space One, with the developed remote agent software system implementing the probe's autonomous task management, mission planning, autonomous execution, condition monitoring, etc. At present, NASA’s Automated Scheduling and Planning Environment system (ASPEN), Extensible Universal Remote Operations Planning Architecture (EUROPA) and ESA’s Advanced Planning and Scheduling Initiative (APSI) have successively served in Earth Observing One and various Mars rover missions.

Due to the complexity of spacecraft systems, complicated constraints, concurrent activities, and uncertain environments, traditional planning techniques are no longer applicable to state-of-the-art space missions. The autonomous planning of space applications faces new challenges, including the following:

  1. Complicated temporal constraints. Most activities must be performed in a specific time window. For example, communication activities must be performed in a certain order, whereas some activities need to be performed in parallel.
  2. Limited resources. The energy and storage capacity of spacecraft is limited, and the resources need to be managed and allocated for use within a certain time interval to ensure the safe operation of the spacecraft and the completion of the mission.
  3. Complex mission objectives. There are many goals that need to be achieved with different values, and reasonable arrangements should be made to obtain the greatest scientific return. At the same time, some planning tasks must be used to find the shortest path timewise or in length, while others require the least fuel consumption to achieve the goal.
  4. Execution uncertainty. There is uncertainty about when to move to a designated location, complete a maintenance operation, or assemble a structure.

To summarize, the research on the autonomous and heuristic planning technology of space applications has the following significance for future space missions:

  1. Reduce ground intervention and reduce spacecraft operating costs and demand for space telemetry networks;
  2. Respond to the uncertainty in space missions and increase the reliability of mission execution;
  3. Adopt advanced mission instructions to enhance the interactivity of ground and spacecraft systems;
  4. Efficient allocation and utilization of various resources of the system for better return of the tasks;
  5. The planning system can be flexibly applied to the design and development of future spacecraft, shortening the development cycle of small spacecraft. 

This Special Issue solicits works in areas of interests that include, but are not limited to, the following:

  • Multi-agent heuristic planning;
  • Moon/Mars/asteroid landing sequence planning;
  • Satellite observing/communication planning and scheduling;
  • Spacecraft payload operation planning;
  • Space station operation and management planning;
  • Spacecraft attitude maneuver planning;
  • Rover path planning;
  • Lander trajectory planning;
  • Autonomous planning applications on space missions.

Prof. Dr. Rui Xu
Prof. Dr. Shengying Zhu
Dr. Zhaoyu Li
Guest Editors

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Published Papers (3 papers)

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Research

24 pages, 2380 KiB  
Article
Application of Optimal Scheduling for Synthetic Aperture Radar Satellite Constellation: Multi-Imaging Mission in High-Density Regional Area
by Kimoon Lee, Dongjin Kim, Daewon Chung and Seonho Lee
Aerospace 2024, 11(4), 280; https://doi.org/10.3390/aerospace11040280 - 2 Apr 2024
Cited by 1 | Viewed by 1436
Abstract
This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house-developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, this research aims to maximize [...] Read more.
This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house-developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, this research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times compared to the greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches. Full article
(This article belongs to the Special Issue Heuristic Planning for Space Missions)
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24 pages, 5295 KiB  
Article
Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning
by Siyao Lu, Rui Xu, Zhaoyu Li, Bang Wang and Zhijun Zhao
Aerospace 2024, 11(4), 253; https://doi.org/10.3390/aerospace11040253 - 24 Mar 2024
Cited by 2 | Viewed by 1416
Abstract
The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to [...] Read more.
The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers’ tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method. Full article
(This article belongs to the Special Issue Heuristic Planning for Space Missions)
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18 pages, 3825 KiB  
Article
Mixed-Integer Linear Programming Model for Scheduling Missions and Communications of Multiple Satellites
by Minkeon Lee, Seunghyeon Yu, Kybeom Kwon, Myungshin Lee, Junghyun Lee and Heungseob Kim
Aerospace 2024, 11(1), 83; https://doi.org/10.3390/aerospace11010083 - 16 Jan 2024
Cited by 1 | Viewed by 1672
Abstract
Satellites have been developed and operated for various purposes. The global satellite market is growing rapidly as the number of satellites and their mission diversity increase. Satellites revolve around the Earth to perform missions and communicate with ground stations repeatedly and sequentially. However, [...] Read more.
Satellites have been developed and operated for various purposes. The global satellite market is growing rapidly as the number of satellites and their mission diversity increase. Satellites revolve around the Earth to perform missions and communicate with ground stations repeatedly and sequentially. However, because satellites are orbiting the Earth, there is a limited time window for missions to a specific area and communication with ground stations. Thus, in an environment where multiple satellites and multiple ground stations (MS-MGs) are operated, scheduling missions and communications to maximize the utilization of satellites is a complex problem. For the MS-MG scheduling problem, this study proposes a mixed-integer linear programming (MILP) model to assign time windows for missions and communications with ground stations to individual satellites. The MILP model is based on the concept of a time-space network and includes constraints reflecting on the space mission environment of satellites. The objective function and constraints of the MILP model were validated through numerical experiments based on actual data from Korean satellites. Full article
(This article belongs to the Special Issue Heuristic Planning for Space Missions)
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