CN108333922B - Single-star autonomous task planning method based on intelligent optimization and constraint reasoning - Google Patents
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Abstract
The invention discloses a single star autonomous task planning method based on intelligent optimization and constraint reasoning, which adopts a method of combining an intelligent optimization algorithm and a reasoning engine to solve a frame, wherein the intelligent optimization algorithm comprises a task sequencing step, a task decomposition step and an activity opportunity search step, the task decomposition step decomposes sequenced tasks into a structured or partially sequenced activity map, the activity opportunity search step carries out scheduling on an imaging subtask and an imaging direction adjustment subtask to obtain a quasi-optimal solution, the activity opportunity search step carries out conflict check on the optimal solution based on the logical relationship, time and satellite resource constraint needles in the activity map, if a conflict exists, the quasi-optimal solution is optimized according to a conflict condition, and local search is carried out on the optimized quasi-optimal solution to obtain the quasi-optimal solution again, and the conflict check is carried out again until a check result passes, or a set maximum number of iterations is reached. The invention generates better task planning result with less calculation amount.
Description
Technical Field
The invention relates to the technical field of remote sensing satellites, in particular to a single-satellite autonomous task planning method based on intelligent optimization and constraint reasoning.
Background
With the increase of hardware level of imaging satellites, the application targets of the imaging satellites have higher requirements. Satellite mission planning has been a difficult point in the field of system engineering because of complex constraints, difficult to predict state information, and a miscellaneous variety of requirements.
The on-satellite autonomous task planning is performed by little relying on external information injection and control, and the intelligent satellite autonomously plans and schedules information acquisition activities of satellite resources according to the sensed self state and external environment to make a satellite earth observation plan meeting the satellite application task requirements.
The imaging satellite autonomous management problem is essentially an artificial intelligence planning problem with resource constraints and time constraints, namely a planning and scheduling integration problem. For imaging satellites, assuming that the domain model contains 10 original propositions, there are 210A state; if the number of actions is large, in the process of searching from an initial state to a target state, each step has many possible action branches. Assuming that the satellite has 10 actions, if it is known in advance that the number of actions required to reach the target state from the initial state is 5,then there are 10 × 9 × 8 × 7 × 6-30240 possible search branches, which are even larger if the number of actions needed is not known in advance. In the problem of autonomous control of the satellite, the action execution is also limited by time constraint, resource constraint and the like, as shown in a satellite plan shown in fig. 1, imaging and return transmission both have great time flexibility, and the solution is still a feasible solution after the start time is adjusted. The planning problem is further complicated if these factors are taken into account in the search.
Therefore, a new simple and computationally inexpensive method for autonomous mission planning is needed to meet the needs of satellite imaging technology.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a single-satellite autonomous task planning method based on intelligent optimization and constraint reasoning, which modularizes a domain model, a time and resource constraint reasoning and problem model and other parts, integrates an attitude control model, a battery model, a solid storage model, an antenna model and other aerospace domain models, and constructs an integrated planning and scheduling framework with expansibility and universality; constructing a single-star autonomous task planning technology based on intelligent optimization and constraint reasoning: the intelligent optimization module takes task and activity opportunity selection as a combined variable to perform local search, and the constraint reasoning module processes and resolves conflict for logical relation, time and resource constraint in the task decomposition activity diagram. The method utilizes heuristic information related to the satellite field and user preference to guide constraint reasoning and plan generation, and generates a better task planning result with less calculation amount.
In order to achieve the purpose, the invention adopts the technical scheme that:
a single-star autonomous task planning method based on intelligent optimization and constraint reasoning adopts a method of combining an intelligent optimization algorithm with a reasoning engine to solve a frame, wherein the intelligent optimization algorithm comprises a task sorting step, a task decomposition step and an activity opportunity searching step, the task sorting step comprises sorting tasks according to selected sorting intelligence, the task decomposition step decomposes the sorted tasks into an activity diagram with a structural or partial sequence, an activity set in the activity diagram only comprises an imaging subtask and an imaging direction adjustment subtask, the activity opportunity searching step schedules the imaging subtask and the imaging direction adjustment subtask to obtain a quasi-optimal solution, the reasoning engine carries out conflict check on the quasi-optimal solution based on logical relation, time and satellite resource constraint in the activity diagram, and if conflict exists, and optimizing the quasi-optimal solution according to the conflict situation, performing local search on the optimized quasi-optimal solution to obtain the quasi-optimal solution again, and performing conflict check again until the check result passes or the set maximum iteration number is reached.
Preferably, the logical relationships in the activity graph include logical requirements and precedence relationships.
Preferably, the on-satellite activities are defined as classes of atomic tasks, the atomic tasks including: the method comprises the following steps of recording, returning, real transmission, daily orientation, ground orientation, solid memory erasing, whole satellite maneuver, camera startup and camera shutdown, wherein the imaging subtasks comprise camera startup and recording camera shutdown, the imaging direction adjusting subtasks comprise ground orientation, conflict checking comprises calling a preset electric quantity model, a whole satellite attitude maneuver model, a data transmission antenna maneuver model, a daily orientation maneuver model, a load code numerical rate calculation model and an imaging condition evaluation model to perform conflict detection, and the models for verification are set based on an atomic task.
Preferably, the constraint reasoning of the reasoning engine comprises logic constraint reasoning, time reasoning and resource constraint reasoning, wherein the logic constraint reasoning adopts a condition triggering mode, and generates and inserts new activities according to conditions; the time reasoning adopts a path consistency check and constraint propagation technology of a time constraint network to reduce a time value range and meet time constraint; the resource constraint reasoning is established on the basis of a time network, the problem is described by the resource time network, the distribution of the resource consumption level is calculated, the defects are found according to the distribution, and the constraint between activities is adjusted on the basis of a defect management mechanism.
Preferably, the smart optimization algorithm is triggered at the following points in time:
(1) t-driven scheduling time points, the T-driven scheduling time points are determined according to a given time interval T, a specific scheduling time point lT is determined, L is more than or equal to 0 and less than or equal to L, LT is less than (L +1) T, and when one scheduling time point lT is reached, a task plan of a next scheduling interval [ lT, (L +1) T ] is calculated and generated, wherein L is a positive integer, T is the given time interval, L is the maximum T-driven scheduling times, and H is a total scheduling interval,
(2)C*driving rescheduling time points, when the satellite operates in a given scheduling interval, if at a certain time t (0 < t < H), the cumulative quantity C of emergency observation tasks on the satellitetExceeding a given threshold C*When it is, the time point is C*Driving rescheduling points in time, wherein the threshold value C*Is a critical cumulative number of emergency observation tasks,
the intelligent optimization algorithm is not triggered at any other time point except the two scheduling time points.
By adopting the technical scheme, compared with the prior art, the single-star autonomous task planning method based on intelligent optimization and constraint reasoning has the beneficial effects that:
1. the invention utilizes heuristic information and user preference related to the satellite field to guide constraint reasoning and plan generation, and generates a better task planning result with less calculation amount.
2. Compared with the traditional satellite scheduling method, the spacecraft knowledge representation method is more convenient to realize continuous updating according to the characteristics of future spacecrafts, and the technical progress is promoted.
Drawings
FIG. 1 is a diagram illustrating exemplary planned subsystem states;
FIG. 2 is a block diagram of the autonomic mission planning solution framework of the present invention;
FIG. 3 is a schematic diagram of an intelligent optimization decision layer according to the present invention;
FIG. 4 is a schematic diagram of a constraint inference decision layer of the present invention;
FIG. 5 is an exploded illustration of a first level of an imaging task of the present invention;
FIG. 6 is an exploded view of a second layer of an observation task according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to specific examples below. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The single-star autonomous task planning method based on intelligent optimization and constraint reasoning adopts a method of combining an intelligent optimization algorithm and a reasoning engine to solve a framework. The intelligent optimization algorithm comprises a task sequencing step, a task decomposition step and an activity opportunity searching step, wherein the task sequencing step comprises sequencing tasks according to the selected sequencing intelligence, the task decomposition step decomposes the sequenced tasks into a structured or partially sequenced activity diagram, an activity set in the activity diagram only comprises an imaging subtask and an imaging direction adjustment subtask, the activity opportunity searching step schedules the imaging subtask and the imaging direction adjustment subtask to obtain a quasi-optimal solution, the inference engine performs conflict check on the quasi-optimal solution based on logical relationship, time and satellite resource constraints in the activity diagram, if a conflict exists, the quasi-optimal solution is optimized according to a conflict condition, and the optimized quasi-optimal solution is subjected to local search to obtain the quasi-optimal solution again and perform the conflict check again, until the verification result passes or the set maximum iteration number is reached.
Preferably, the logical relationships in the activity graph include logical requirements and precedence relationships.
Preferably, the on-satellite activities are defined as classes of atomic tasks, the atomic tasks including: the method comprises the following steps of recording, returning, real transmission, daily orientation, ground orientation, solid memory erasing, whole satellite maneuver, camera startup and camera shutdown, wherein the imaging subtasks comprise camera startup and recording camera shutdown, the imaging direction adjusting subtasks comprise ground orientation, conflict checking comprises calling a preset electric quantity model, a whole satellite attitude maneuver model, a data transmission antenna maneuver model, a daily orientation maneuver model, a load code numerical rate calculation model and an imaging condition evaluation model to perform conflict detection, and the models for verification are set based on an atomic task.
Preferably, the constraint reasoning of the reasoning engine comprises logic constraint reasoning, time reasoning and resource constraint reasoning, wherein the logic constraint reasoning adopts a condition triggering mode, and generates and inserts new activities according to conditions; the time reasoning adopts a path consistency check and constraint propagation technology of a time constraint network to reduce a time value range and meet time constraint; the resource constraint reasoning is established on the basis of a time network, the problem is described by the resource time network, the distribution of the resource consumption level is calculated, the defects are found according to the distribution, and the constraint between activities is adjusted on the basis of a defect management mechanism.
Preferably, the smart optimization algorithm is triggered at the following points in time:
(1) t-driven scheduling time points, the T-driven scheduling time points are determined according to a given time interval T, a specific scheduling time point lT is determined, L is more than or equal to 0 and less than or equal to L, LT is less than (L +1) T, and when one scheduling time point lT is reached, a task plan of a next scheduling interval [ lT, (L +1) T ] is calculated and generated, wherein L is a positive integer, T is the given time interval, L is the maximum T-driven scheduling times, and H is a total scheduling interval,
(2)C*driving rescheduling time points, when the satellite operates in a given scheduling interval, if at a certain time t (0 < t < H), the cumulative quantity C of emergency observation tasks on the satellitetExceeding a given threshold C*When it is, the time point is C*Driving rescheduling points in time, wherein the threshold value C*Is a critical cumulative number of emergency observation tasks,
the intelligent optimization algorithm is not triggered at any other time point except the two scheduling time points.
By adopting the technical scheme, compared with the prior art, the single-star autonomous task planning method based on intelligent optimization and constraint reasoning has the beneficial effects that:
1. the invention utilizes heuristic information and user preference related to the satellite field to guide constraint reasoning and plan generation, and generates a better task planning result with less calculation amount.
2. Compared with the traditional satellite scheduling method, the spacecraft knowledge representation method is more convenient to realize continuous updating according to the characteristics of future spacecrafts, and the technical progress is promoted.
The invention relates to a single-star autonomous task planning method based on intelligent optimization and constraint reasoning, which comprises the following contents:
(1) and establishing a spacecraft knowledge representation method. In the traditional satellite scheduling research, various using operation rule constraints of the spacecraft are directly applied to the scheduling process, so that the spacecraft is inconvenient to update continuously according to the characteristics of the future spacecraft; an extensible knowledge representation method needs to be researched according to the characteristics of the spacecraft and is used for subsequent modeling and debugging.
(2) And (4) an autonomous mission planning solution framework. A basic solution framework using a combination of inference engines and intelligent optimization algorithms is shown in fig. 2. The intelligent optimization algorithm takes the selection of tasks and activity opportunities as a combined variable to carry out local search, and the bottom layer adopts a constraint reasoning method to carry out processing and conflict resolution on logical relations, time and resource constraints in an activity diagram of task decomposition. Constraint checking does not make a decision, only provides current constraint satisfaction or conflict conditions.
The intelligent optimization algorithm includes task sequencing, task decomposition and activity opportunity search, as shown in FIG. 3. Firstly, selecting a sorting criterion from different sorting criteria by a user, such as priority, demand degree, earliest starting time, latest ending time, free time, resource contention degree and the like; the task decomposition process decomposes tasks into structured or partially ordered active sets; methods such as heuristic selection based on sorting, intelligent selection based on knowledge, random selection and the like exist in the process of searching the active opportunity. The optimization result is a structured or partially ordered set of activities, i.e., the activity graph of FIG. 3. Here the activities select opportunities, i.e. one time window per activity.
Constraint reasoning includes logical constraint reasoning, temporal reasoning and resource constraint reasoning, as shown in FIG. 4. The logical reasoning mainly adopts condition triggering, and new activities are generated and inserted according to the conditions. The time reasoning mainly adopts the path consistency check and constraint propagation technology of the time constraint network to realize time value range reduction and time constraint satisfaction. Resource reasoning is based on a time network, describing problems with a resource-time network, requiring the calculation of a distribution of resource consumption levels as activities change resource states in a relative manner, finding defects from the distribution, and adjusting activities and inter-activity constraints based on a defect management mechanism.
(3) A task pattern library. In the future, executable tasks of the aerospace system are more refined, and the tasks comprise multiple modes such as multi-point target imaging, three-dimensional imaging, wide splicing imaging, dynamic scanning imaging and the like. These imaging modalities exhibit the following characteristics: one contains unknown, uncertain path selection or action sequences; secondly, an ambiguous and difficult-to-predict relation exists between the task path and the result; thirdly, a complex task may comprise a series of dependent sub-tasks, and thus there may be a complex internal relationship between the sub-tasks.
The method defines 5 actions in the established satellite planning field model, namely, the actions are atomic tasks in the planning process; two levels of complex tasks are defined on the basis of the atomic task, the first level is imaging, which means a camera imaging task and is completely composed of the atomic task, the corresponding decomposition method is d _ imaging, and the first level decomposition of the imaging task is shown in fig. 5.
The second layer is an observing task, which is a satellite observing task and is composed of an imaging task and other atomic tasks, two decomposition methods are provided according to preconditions, which are d _ observing _1 and d _ observing _2, respectively, and an example of the decomposition of the second layer of the observing task is shown in fig. 6.
(4) And scheduling the resource knowledge model. The domain knowledge is the basis of problem modeling and solving, and particularly the domain modeling, search guidance and the like need domain knowledge support, so that the analysis of spacecraft system composition, on-satellite resources, functions and constraint conditions is performed, and the establishment of a domain knowledge base comprising a domain model, heuristic information and preference knowledge is the basis of the research of the invention. The invention focuses on explicit knowledge, so that explicit knowledge modeling such as activities, constraints, heuristic information, preference functions and the like in spacecraft management needs to be broken through in a key way. In the intelligent satellite researched by the subject, objects are used for representing all subsystems of the satellite and observing target entities, predicates are used for representing state attributes of all subsystems, initial states define state values of all subsystems at the beginning, target descriptions define state values of all subsystems, which are reached by task requirements, and state value transformation is realized by action execution.
(5) A subsystem prediction model. In the satellite task planning, not only a task observation and data transmission scheme needs to be planned and arranged, but also a satellite action sequence corresponding to the task observation and data transmission scheme needs to be optimized and generated, wherein the satellite action sequence comprises a plurality of satellite actions such as recording, returning, real transmission, sun orientation, earth orientation, solid memory erasing, whole satellite maneuvering, camera starting, camera shutdown and the like, the actions relate to complex working principles and use constraints of a satellite power supply, load, storage, attitude control, data transmission and other systems, and if the satellite action sequence is not considered or simplified through hypothesis, the feasibility of the satellite task planning scheme is influenced. In the satellite task planning modeling and solving process, prediction models of all subsystems of related satellites are required to be called, and the prediction models comprise an electric quantity model, a whole satellite attitude maneuver model, a data transmission antenna maneuver model, a daily directional maneuver model, a load code data rate calculation model and an imaging condition evaluation model. These models need to be designed and developed based on the actual characteristics of the various subsystems.
The method modularizes a domain model, a time and resource constraint reasoning and problem model and other parts, integrates an attitude control model, a battery model, a solid storage model, an antenna model and other aerospace domain models, and constructs an integrated planning and scheduling framework with expansibility and universality; constructing a single-star autonomous task planning technology based on intelligent optimization and constraint reasoning: the intelligent optimization module takes task and activity opportunity selection as a combined variable to perform local search, and the constraint reasoning module processes and resolves conflict for logical relation, time and resource constraint in the task decomposition activity diagram.
The foregoing detailed description is given by way of example only, to better enable one of ordinary skill in the art to understand the patent, and is not to be construed as limiting the scope of what is encompassed by the patent; any equivalent alterations or modifications made according to the spirit of the disclosure of this patent are intended to be included in the scope of this patent.
Claims (4)
1. A single star autonomous task planning method based on intelligent optimization and constraint reasoning is characterized in that a solution frame is obtained by combining an intelligent optimization algorithm with a reasoning engine, the intelligent optimization algorithm comprises a task sorting step, a task decomposition step and an activity opportunity search step, the task sorting step comprises sorting tasks according to selected sorting intelligence, the task decomposition step decomposes the sorted tasks into a structured or partially ordered activity map, an activity set in the activity map only comprises an imaging subtask and an imaging direction adjustment subtask, the activity opportunity search step schedules the imaging subtask and the imaging direction adjustment subtask to obtain a quasi-optimal solution, and the reasoning engine performs conflict check on the quasi-optimal solution based on logical relation, time and satellite resource constraint in the activity map, if the conflict exists, optimizing the quasi-optimal solution according to the conflict condition, carrying out local search on the optimized quasi-optimal solution to obtain the quasi-optimal solution again, and carrying out conflict check again until the check result passes or the set maximum iteration times is reached;
defining the on-satellite activities as a plurality of classes of atomic tasks, wherein the atomic tasks comprise: the method comprises the following steps of recording, returning, real transmission, sun orientation, ground orientation, solid memory erasing, whole satellite maneuver, camera startup and camera shutdown, wherein the imaging subtask comprises camera startup, recording and camera shutdown, the imaging direction adjusting subtask comprises ground orientation, and the conflict check comprises calling a preset electric quantity model, a whole satellite attitude maneuver model, a data transmission antenna maneuver model, a sun orientation maneuver model, a load code numerical rate calculation model and an imaging condition evaluation model to perform conflict detection, wherein each model for verification is set based on an atomic task.
2. The method of claim 1, wherein the logical relationships in the activity graph include logical requirements and precedence relationships.
3. The method for planning the single-star autonomous task based on the intelligent optimization and the constraint reasoning is characterized in that the constraint reasoning of the reasoning engine comprises logic constraint reasoning, time reasoning and resource constraint reasoning, the logic constraint reasoning adopts a condition triggering mode, and new activities are generated and inserted according to conditions; the time reasoning adopts a path consistency check and constraint propagation technology of a time constraint network to reduce a time value range and meet time constraint; the resource constraint reasoning is established on the basis of a time network, the problem is described by the resource time network, the distribution of the resource consumption level is calculated, the defects are found according to the distribution, and the constraint between activities is adjusted on the basis of a defect management mechanism.
4. The method for single-star autonomous mission planning based on intelligent optimization and constraint reasoning according to any of claims 1-3, characterized in that the intelligent optimization algorithm is triggered at the following points in time:
(1) t-driven scheduling time points, the T-driven scheduling time points are determined according to a given time interval T, a specific scheduling time point lT is determined, L is more than or equal to 0 and less than or equal to L, LT is less than (L +1) T, and when one scheduling time point lT is reached, a task plan of a next scheduling interval [ lT, (L +1) T ] is calculated and generated, wherein L is a positive integer, T is the given time interval, L is the maximum T-driven scheduling times, and H is a total scheduling interval,
(2)C*driving rescheduling time points, when the satellite operates in a given scheduling interval, if at a certain time t (0 < t < H), the cumulative quantity C of emergency observation tasks on the satellitetExceeding a given threshold C*When it is, the time point is C*Driving rescheduling points in time, wherein the threshold value C*Is a critical cumulative number of emergency observation tasks,
the intelligent optimization algorithm is not triggered at any other time point except the two scheduling time points.
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| CN109470267A (en) * | 2018-11-02 | 2019-03-15 | 佛山科学技术学院 | A kind of attitude of satellite filtering method |
| CN109947058B (en) * | 2019-02-15 | 2020-08-18 | 北京空间飞行器总体设计部 | Spacecraft multiple autonomous management function state control method |
| CN111338783B (en) * | 2020-01-17 | 2022-09-06 | 北京理工大学 | Method for automatically generating multi-priority task execution sequence of spacecraft |
| CN112068580B (en) * | 2020-09-10 | 2021-11-23 | 北京理工大学 | Method for rapidly detecting action set with logic conditions not meeting requirements in spacecraft planning sequence |
| CN114604443B (en) * | 2022-02-17 | 2023-06-06 | 中国电子科技集团公司第十研究所 | Multi-star measurement and control scheduling method and system under real and complex constraint conditions |
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