Mission Planning Algorithms Research Articles

Networked Radar Mission Planning Algorithm Based on Pulse Interleaving and Cross-Scheduling Interval

Aiming at the problem of networked radar task planning, this paper proposes a task scheduling algorithm based on pulse interleaving and cross-scheduling interval from the perspective of rationally arranging communication and detection tasks, improving the utilization rate of radar system time resources and the success rate of task scheduling. The waiting period of tracking tasks is fully utilized to sort the tasks by pulse interleaving. On the other hand, taking advantage of the characteristics of large search task cycle and long timeliness, when there are still idle time slices in the current scheduling cycle that are not used, the time axis can be opened, and the subsequent scheduling interval can be The search tasks in the interval are advanced to be executed within the current scheduling cycle. The simulation results show that the algorithm can effectively improve the time resource utilization rate and task scheduling success rate of the system.

Study on mission planning algorithm for multi-target passive tracking based on satellite formation

Mission planning for Space Situational Awareness (SSA) is challenging when only passive optical sensors are available to observe the targets. In this study, a mission-planning algorithm using a passive optical sensor for multi-target tracking in high altitude orbits based on microsatellite formation was developed, and a two-layer iterative consensus-based auction algorithm was designed to solve the optimization problem with requirements and constraints. First, orbit estimation was formulated using the perturbed dynamics and the line-of-sight angle measurement model. Second, distributed multi-target tracking tasks, including tasking metrics and observation constraints, were constructed for planning. Then, a two-layer iterative consensus-based auction algorithm was developed to solve the mission planning and conflict problem of multi-target tracking using multiple satellites. The only requirement for the algorithm was information exchange between neighboring observers, which encompasses the limitations of centralized planning and facilitates the execution of tasks more efficiently and flexibly. In addition, an event-triggered collaborative filtering approach suitable for this scenario was proposed to solve the distributed estimation problem of measurement asynchrony in epoch and frequency from different observers. Finally, numerical simulations were conducted under different conditions to verify the feasibility of the proposed method.

Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning

Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.

Dynamic Mission Planning Algorithm for UAV Formation in Battlefield Environment

The limited time duration and spatial instability of TSTs increase the difficulty of mission planning for unmanned aerial vehicle (UAV) formations, which in turn affects the task execution abilities of UAVs. Aiming at this problem, a multi-UAV dynamic mission planning algorithm based on the time window mechanism is proposed under a distributed formation structure. The proposed algorithm comprehensively considers a variety of complex constraints involved in actual application scenarios and constructs a UAV formation mission planning model with minimum task execution cost and path planning cost as the optimization goal. In this work, particle swarm optimization algorithm is improved by introducing a specific particle coding method and a competitive co-evolutionary population updating strategy, to enhance the solution speed and accuracy of the mission planning model. In this way, the optimal task allocation strategy and task execution path are obtained to meet the mission requirements, and to achieve the multi-UAV cooperative reconnaissance, strike and evaluation of multiple TSTs. Furthermore, the effectiveness of the proposed algorithm is validated via simulations.

Multi-satellite Mission Planning Algorithm Based on Preemptive Priority Model

<p>Aiming at the problems of high time-consuming and insufficient guarantee of high-priority task completion in the large-scale multi-satellite multi-observation mission planning application, a multi-satellite multi-mission planning model based on preemptive priority model is proposed. Combined with the whole-neighborhood greedy search strategy, an improved tabu search algorithm is designed, taking the combination of satellite observation windows as the decision variables. The designed algorithm realizes an efficient solver to the proposed mission planning model. The simulation results show that the average solving time of the algorithm is 144s under the problem scale of 100 satellites and 3000 tasks. Compared with the existing algorithm, the calculation time is shortened by at least 75% under the premise that the total number of planning tasks is comparable, which is more in line with the high time efficiency requirements for satellite mission planning in engineering applications.</p> <p> </p>

Autonomous mission planning of Earth observation satellite based on onboard cloud detection

The imaging results of Earth Observation Satellite (EOS) with optical camera are usually affected by meteorological parameters, such as clouds that block the ground target and make the images useless. In order to achieve effective images, research on minimizing the impact of cloud coverage has been conducted for several years, and many methods have been proposed. Cloud detection and mission planning are two typical approaches to be considered. However, results of cloud detection are influenced by cloud status, such as cloud movement. Moreover, performance of mission planning algorithms is related to the computation environment, especially for those implemented onboard. By considering the above limitations, an autonomous mission planning method for the EOS based on onboard cloud detection is proposed in this paper. In order to detect the cloud coverage above the ground target, the cloud-detecting sensor is utilized before satellite taking images. The approach to analyze the cloud detection area of the ground target is introduced. Then, the effective observation window of the ground target is edited according to the cloud detection results. Moreover, the imaging plan of the ground target is re-planned based on the edited effective observation window accordingly. Mathematical simulations are implemented. And the result of simulations shows that the proposed method is able to update accurate effective observation window of the ground target, and the imaging plans are adjusted appropriately which increases the validity of images taken by the satellite.

Emerging approaches to support dynamic mission planning: survey and recommendations for future research

In the Department of Defense, unmanned aerial vehicle (UAV) mission planning is typically in the form of a set of pre-defined waypoints and tasks, and results in optimized plans being implemented prior to the beginning of the mission. These include the order of waypoints, assignment of tasks, and assignment of trajectories. One emerging area that has been recently identified in the literature involves frameworks, simulations, and supporting algorithms for dynamic mission planning, which entail re-planning mid-mission based on new information. These frameworks require algorithmic support for flight path and flight time approximations, which can be computationally complex in nature. This article seeks to identify the leading academic algorithms that could support dynamic mission planning and recommendations for future research for how they could be adopted and used in current applications. A survey of emerging UAV mission planning algorithms and academic UAV flight path algorithms is presented, beginning with a taxonomy of the problem space. Next, areas of future research related to current applications are presented.

SAR위성의 영상획득 시나리오 모델링 및 임무설계 알고리즘 성능검증

현대 사회에서 인공위성은 통신, 영상 등의 분야에서 널리 이용되고 있다. 이 중에서도 인공위성을 통해 획득한 영상은 넓은 지역에 대한 다양한 정보를 담고 있어 농업, 자원개발 및 활용, 군사적 목적 등으로 활용되고 있다. 인공위성의 특성상 영상을 획득할 수 있는 시간이 매우 제한적이므로 주어진 시간 내에 최대한 효율적인 영상획득을 수행하는 것이 중요하다. 이를 위해서 본 연구에서는 인공위성이 영상을 획득하는 데 소요되는 시간 및 자세 기동에 대한 모델링을 수행하고 이를 바탕으로 휴리스틱 평가함수를 이용한 임무설계 알고리즘을 제안하고 수치 시뮬레이션을 통하여 해당 알고리즘의 성능을 검증하였다.

Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto

A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area, and with a comparable ground-sampling distance (GSD) of ≈2.4 cm/px (≈1.5 cm/px when adjusted for camera pixel density). The resulting three time-lapse models provide valuable metrics for ground motion, progression of damage, resilience of the village, and the recovery progress over a span of two years.

A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs

This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem — heuristics for task assignment and all permutations for sequencing, and (2) large sized problem — heuristics for both task assignment and sequencing. The proposed algorithms have been implemented and tested. Numerical experimental results show that the proposed algorithm is very efficient and can effectively solve relatively big problems.

Optimal Cruise Altitude for Aircraft Thermal Management

Mission-planning algorithms and trajectory optimization methods for aircraft that use fuel as a heat sink must ensure that fuel temperature limits are not violated at any point in a flight. The problem of determining the cruise altitude that minimizes final fuel tank temperature for jet aircraft that use recirculating fuel to remove waste thermal energy from the aircraft is considered. A heat exchanger transfers energy from the aircraft subsystems to the fuel. A portion of the fuel is burned in a turbojet engine, whereas the remaining unburned fuel is passed through a cooler that transfers some heat from the fuel to the atmosphere. A differential equation that describes the time rate of change of fuel tank temperature is derived based on the principles of conservation of mass and energy. Specific conditions are derived for the cruise altitude that minimizes final tank temperature for flight conditions associated with maximum endurance and range. Expressions are also derived that provide estimates of time and range to a temperature limit. Numerical examples are provided to illustrate the utility of the conditions and to assess the impact of the assumptions made in the derivation of the analytic models. The conditions also provide a relationship between aircraft performance parameters, the standard atmosphere, and the heat transfer characteristics of the fuel recirculation loop.

An onboard software decisional architecture for Rapid Environmental Assessment missions

AbstractA software decisional architecture implemented and tested on the Daurade Autonomous Underwater Vehicle designed to achieve covert Rapid Environmental Assessment missions is presented. This decisional architecture is based on a software environment meant for controlling and monitoring highly autonomous systems using interpreted Petri nets and mission planning algorithms; when disruptive events occur, alternative encoded reactions are triggered and the current plan is modified. Lab and sea tests have validated data transmission, planning algorithms, online supervision of the AUV navigation actions and decisional capabilities while reacting to asynchronous events. Daurade is now used for operational REA missions.

Mission Planning and Flight Control: Meeting the Challenge with Intelligent Techniques

This paper presents a hybrid hardware/software platform that supports flight control and mission planning algorithms for an autonomous helicopter. The emphasis is on the use of intelligent fuzzy logic based techniques and an object-modeling approach to account for unmodeled dynamics to address uncertainty issues and to provide a flexible platform for development purposes and an operator interface. Fuzzy logic routines are implemented in such critical vehicle modules as the route planner the fuzzy navigator the fault-tolerant tools and the flight controller.