scholarly journals UAV Mission Planning with SAR Application

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1080 ◽  
Author(s):  
Wojciech Stecz ◽  
Krzysztof Gromada

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

Author(s):  
Yannik Rist ◽  
Michael A. Forbes

This paper proposes a new mixed integer programming formulation and branch and cut (BC) algorithm to solve the dial-a-ride problem (DARP). The DARP is a route-planning problem where several vehicles must serve a set of customers, each of which has a pickup and delivery location, and includes time window and ride time constraints. We develop “restricted fragments,” which are select segments of routes that can represent any DARP route. We show how to enumerate these restricted fragments and prove results on domination between them. The formulation we propose is solved with a BC algorithm, which includes new valid inequalities specific to our restricted fragment formulation. The algorithm is benchmarked on existing and new instances, solving nine existing instances to optimality for the first time. In comparison with current state-of-the-art methods, run times are reduced between one and two orders of magnitude on large instances.


Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5712
Author(s):  
Wojciech Stecz ◽  
Krzysztof Gromada

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.


Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 313
Author(s):  
Nicolas Dupin ◽  
Rémi Parize ◽  
El-Ghazali Talbi

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.


Author(s):  
О.І. Тимочко ◽  
А.В. Тристан ◽  
О.Є. Чернавіна ◽  
А.О. Бережний

The flight route planning method for an unmanned aerial vehicle (UAV) to search for a dynamic object in a forest-steppe area was developed. Indicators and criteria of search efficiency with the participation of UAV were proposed. They allow you to choose the route that most fully meets the goals and objectives of the search. A numerical estimates of the search efficiency indicator were obtained for choosing a rational UAV flight route under conditions of uncertain behavior of a dynamic object.


2014 ◽  
Vol 02 (01) ◽  
pp. 73-86 ◽  
Author(s):  
L. Geng ◽  
Y. F. Zhang ◽  
Jingjing Wang ◽  
Jerry Y. H. Fuh ◽  
S. H. Teo

In this paper, a mission planning system is developed for managing multiple unmanned aerial vehicles (UAVs) of various capabilities to execute a series of missions over multiple targets. A target may need up to three tasks (i.e., classification, attack, and verification) to be carried out in sequence. The problem is addressed in two decision-making stages. First, the shortest feasible flying path for a UAV to fly between any pair of task locations is obtained using a customized A* algorithm. During the search, constraints such as collision avoidance from terrain, circumvention of flight prohibition zones, and the flying capabilities of UAVs are considered. In the second stage, with the obtained UAV flying paths between task locations, mission planning is modeled as a vehicle routing problem (VRP) with time window and precedence requirements. A genetic algorithm with a built-in mixed integer programming (MIP) solver is developed to solve the problem. The outputs of this proposed planning system include (1) task assignment among the UAVs, (2) mission execution schedule for each UAV, and (3) flying paths of the UAVs.


Author(s):  
Y. Chen ◽  
X. Shen ◽  
G. Zhang ◽  
T. Liu ◽  
Z. Lu ◽  
...  

Abstract. Global warming has become one of the most prominent global issues, and Antarctic ice sheet is one of the indicator of global climate change. Satellite imagery has become an important means of monitoring the changes in Antarctic ice sheet. Due to the high overlap of satellite imaging swaths, the existing Antarctica images have the disadvantages of long period of imagery acquisition, large temporal difference among the mosaic images, and low utilization of satellite resource. This paper proposes a satellite imaging mission planning method for fast Antarctica coverage. First, the imaging time window is forecasted within the specified imaging time range to obtain all the visible time windows of the imaging satellite to Antarctica. Then, taking the selection of each time window and the satellite swing angle in each time window as decision variables, and the satellite attitude maneuver ability as constraint, an imaging mission model including two objective functions with minimum number of imaging time windows and the maximum coverage rate is established. To solving the proposed multi-objective optimization model, an improved real-binary hybrid LMOCSO (large-scale multi-objective optimization based on a competitive swarm optimizer) is proposed in this paper. Finally, a simulation experiment was performed using Gaofen-3 satellite to verify the effectiveness of the proposed method.


2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xi Jiang ◽  
Haijun Mao ◽  
Hao Zhang

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier’s total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route.


2018 ◽  
Vol 17 (04) ◽  
pp. 505-513 ◽  
Author(s):  
H. Savitri ◽  
D. A. Kurniawati

CV. Jogja Transport is a company that distribute cakes “Sari Roti” in Yogyakarta, Indonesia. It has responsibility to distribute the cakes for every customer during the customers’ time windows. The distribution problem of CV. Jogja Transport belongs to Vehicle Routing Problem with Time Window (VRPTW). This paper tries to solve the problem of CV. Jogja Transport by proposing “cluster first route second” algorithm of simple heuristic method. Then the algorithm is combined with sweep algorithm for clustering the customers and Mixed Integer Linear Programming (MILP) to select the best route so that it can minimize the distance of each cluster. The result indicate that implementation of sweep algorithm and MILP can reduce the distances and the fuel up to 10.95% and the travel distance up to 2.60%.


2015 ◽  
Vol 03 (03) ◽  
pp. 205-219 ◽  
Author(s):  
Jingjing Wang ◽  
Y. F. Zhang ◽  
L. Geng ◽  
J. Y. H. Fuh ◽  
S. H. Teo

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.


Author(s):  
Di Wang ◽  
Mengqi Hu ◽  
Yang Gao

Recent years have witnessed a tremendous growth of interest in multi-robot system which can execute more complex tasks compared to single robot. To improve the operational life of multi-robot system and address challenges in long-duration mission, the solar-powered multi-robot system has been demonstrated to be an effective solution. To ensure efficient operation of solar-powered multi-robot system, we propose a multi-criteria mixed integer programming model for multi-robot mission planning to minimize three objectives including traveling distance, traveling time, and net energy consumption. Our proposed model is an extension of multiple vehicle routing problem considering time window, flexible speed, and energy sharing where a set of flexible speeds are proposed to explore the influence of robot’s velocity on energy consumption and solar energy harvesting. Three sets of case studies are designed to investigate the tradeoffs among the three objectives. The results demonstrate that heterogeneous multi-robot system: 1) can more efficiently utilize solar energy and 2) need a multi-criteria model to balance the three objectives.


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