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In this thesis, both off-line and online coordinated path planning for Unmanned Aerial Vehicles (UAVs) are studied. These problems have emerged due to the increasing needs for UAVs in both military and civil applications. To accomplish a certain objective, both the path planning for a single UAV and for multiple UAVs have been examined. Although there are previous studies in this field, we focus on maximizing the collected information instead of minimizing the total mission time. Studies carried out in this thesis can be divided into two main headings as offline route planning and online route planning. Under offline path planning, path planning problem is studied for a single UAV, firstly. Along the designed path, the objectives are to maximize the collected information from Desired Regions (DR) while avoiding flying over Forbidden Regions (FR) and reaching the destination. So as to realize this objective, a novel off-line path planning algorithm is proposed. This algorithm, unlike the methods proposed in the literature to date, covers operators that mimic the behavior of the human path planner. The obtained results provide the need for identification of problem-specific operators for further studies in task planning. In addition, the algorithm produces nearly global optimum solution through the intermediate steps, providing a path-search-space reduction. Secondly, the proposed algorithm has been developed for off-line path planning of multiple UAVs and path planning in 3D environment. Development for methods of reducing the search space are at the basis of these studies as with the proposed algorithm for a single UAV. The problem of path planning for multiple UAVs is modeled as multiple Traveling Salesman Problem (mTSP), then the problem is considered as multiple single-UAV-Path-Planning-Problem. The other problem studied is the online path planning for multiple UAVs. In this problem, how to plan the paths of each UAV to maximize the instantaneous collected amount information from desired regions is examined. Maximization of information is accomplisehed by the coordinated guidance of multiple UAVs. The coordination is performed by assignment of regions to UAVs, instantaneously. Assignment process is realized by the designation of centralized decision maker.Ph.D. - Doctoral Progra

Control structure design with constraints for a slung load quadrotor system

We propose a control structure for a quadrotor carrying a slung load with swing-angle constraints. This quadrotor is supposed to pass through the waypoints at specified speeds. First, a cascaded PID autopilot is designed, which adaptively gives attention to position and speed requirements as a function of their errors. Its parameters are found from an optimization problem solved using the PSO algorithm. Second, this controller’s performance is improved by adding the Complementary Controller employing an ANN. 5. Training data for the ANN is created by solving optimal control problems. The ANN is activated when the swing angle constraint is about to be violated. It is trained using optimal control values corresponding to the cases where the swing angle falls in a particular band about the upper swing angle constraint. Simulations are performed in a MATLAB environment. Finally, some of the simulation results are validated on a physical system

3D Path Planning for Multiple UAVs for Maximum Information Collection

This paper addresses the problem of path planning for multiple UAVs. The paths are planned to maximize collected amount of information from Desired Regions (DR) while avoiding Forbidden Regions (FR) violation and reaching the destination. The approach extends prior study for multiple UAVs by considering 3D environment constraints. The path planning problem is studied as an optimization problem. The problem has been solved by a Genetic Algorithm (GA) with the proposal of novel evolutionary operators. The initial populations have been generated from a seed-path for each UAV. The seed-paths have been obtained both by utilizing the Pattern Search method and solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of "which DR should be visited by which UAV". It should be emphasized that all of the paths in population in any generation of the GA have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented in Section 6. Although there are previous studies in this field, this paper focuses on maximizing the collected information instead of minimizing the total mission time. Even though, a direct comparison of our results with those in the literature is not possible, it has been observed that the proposed methodology generates satisfactory and intuitively expected solutions

Refining the progressive multiple sequence alignment score using genetic algorithms

Given a set of N (N > 2) sequences, the Multiple Sequence Alignment (MSA) problem is to align these N sequences, possibly with gaps, that bring out the best score due to a given scoring criterion between characters. Multiple sequence alignment is one of the basic tools for interpreting the information obtained from bioinformatics studies. Dynamic Programming (DP) gives the optimal alignment of the two sequences for the given scoring scheme. But, in the case of multiple sequence alignment it requires enormous time and space to obtain the optimal alignment. The time and space requirement increases exponentially with the number of sequences. There are two basic classes of solutions except the DP method: progressive methods and iterative methods. In this study, we try to refine the alignment score obtained by using the progressive method due to given scoring criterion by using an iterative method. As an iterative method genetic algorithm (GA) has been used. The sum-of-pairs (SP) scoring system is used as our target of optimization. There are fifteen operators defined to refine the alignment quality by combining and mutating the alignments in the alignment population. The results show that the novel operators, sliding-window, local-alignment, which have not been used up to now, increase the score of the progressive alignment by amount of % 2

Online path planning for unmanned aerial vehicles to maximize instantaneous information

© The Author(s) 2021.In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented

3D path planning for unmanned aerial vehicles

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path, the objectives are to maximize the collected information from Desired Regions (DR) while avoiding flying over Forbidden Regions (FR) and reaching the destination. In this paper, the path planning problem for a multiple Unmanned Air Vehicles (UAVs) is studied with the proposal of novel evolutionary operators. The initial populations seed-path for each UAV have been obtained both by utilizing the Pattern Search method and solving the multiple Traveling Salesman Problem (mTSP). Utilizing the mTSP solves the assignment problem of which DR should be visited by which UAV. It should be emphasized that all of the paths in population in any generation of the evolutionary algorithm (EA) have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented

Evaluation of Appropriateness of the Score Functions Used In Multiple Sequence Alignments Problem

In this study, the effects of the scoring functions, which are used in Multiple Sequence Alignment problem, to find the biologically meaningful alignment has been investigated. BALiBASE version 3 has been used as benchmark set. Results have been obtained by calculating score values for alignments in benchmark set and investigating whether the higher score is possible for benchmark alignment or not

Path Planning for UAVs for Maximum Information Collection

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In addition to these newly proposed operators, standard mutation and crossover operators are used. The initial population seed-path is obtained by both utilizing the pattern search method and solving the traveling salesman problem (TSP). Using this seed-path the initial population of paths is generated by randomly selected heading angles. It should be emphasized that all of the paths in population in any generation of the genetic algorithm (GA) are constructed using the dynamical mathematical model of a UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm is tested with different scenarios, and the results are presented in Section VI. Although there are previous studies in this field, the focus here is on maximizing the CI instead of minimizing the total mission time. In addition it is observed that the proposed operators generate better paths than classical evolutionary operators

3D Path Planning for UAVs for Maximum Information Collection

This paper addresses the problem of path planning for multiple UAVs. The paths are planned to maximize collected amount of information from Desired Regions (DR) while avoiding Forbidden Regions (FR) violation and reaching the destination. The approach extends prior study for multiple UAVs by considering 3D environment constraints. The path planning problem is studied as an optimization problem. The problem has been solved by a Genetic Algorithm (GA) with the proposal of novel evolutionary operators. The initial populations have been generated from a seed-path for each UAV. The seed-paths have been obtained both by utilizing the Pattern Search method and solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of "which DR should be visited by which UAV". It should be emphasized that all of the paths in population in any generation of the GA have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented in Section V. Although there are previous studies in this field, this paper focuses on maximizing the collected information instead of minimizing the total mission time. Even though, a direct comparison of our results with those in the literature is not possible, it has been observed that the proposed methodology generates satisfactory and intuitively expected solutions. Keyword

Coordinated guidance for multiple UAVs

This paper addresses the path planning problem of multiple unmanned aerial vehicles (UAVs). The paths are planned to maximize collected amount of information from desired regions (DRs), while avoiding forbidden regions (FRs) and reaching the destination. This study focuses on maximizing collected information instead of minimizing total mission time, as in previous studies. The problem is solved by a genetic algorithm (GA) with the proposal of novel evolutionary operators. The initial populations are generated from a seed-path for each UAV. The seed-paths are obtained both by utilizing the pattern search method and by solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of which DR should be visited by which UAV?' All of the paths in the population in any generation of the GA are constructed using a dynamical UAV model. Simulations are realized in a MATLAB/Simulink environment for different mission scenarios and the results provide physically realizable flight paths, which visit DRs and avoid FRs. Real-world experiments are conducted by using small UAVs, which are constructed by autopilot integration on model airplanes. Flight tests performed based on simulated scenarios proved beneficial in maximizing the collected amount of information for multiple UAV missions