375 research outputs found

    A review of optimization techniques in spacecraft flight trajectory design

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    For most atmospheric or exo-atmospheric spacecraft flight scenarios, a well-designed trajectory is usually a key for stable flight and for improved guidance and control of the vehicle. Although extensive research work has been carried out on the design of spacecraft trajectories for different mission profiles and many effective tools were successfully developed for optimizing the flight path, it is only in the recent five years that there has been a growing interest in planning the flight trajectories with the consideration of multiple mission objectives and various model errors/uncertainties. It is worth noting that in many practical spacecraft guidance, navigation and control systems, multiple performance indices and different types of uncertainties must frequently be considered during the path planning phase. As a result, these requirements bring the development of multi-objective spacecraft trajectory optimization methods as well as stochastic spacecraft trajectory optimization algorithms. This paper aims to broadly review the state-of-the-art development in numerical multi-objective trajectory optimization algorithms and stochastic trajectory planning techniques for spacecraft flight operations. A brief description of the mathematical formulation of the problem is firstly introduced. Following that, various optimization methods that can be effective for solving spacecraft trajectory planning problems are reviewed, including the gradient-based methods, the convexification-based methods, and the evolutionary/metaheuristic methods. The multi-objective spacecraft trajectory optimization formulation, together with different class of multi-objective optimization algorithms, is then overviewed. The key features such as the advantages and disadvantages of these recently-developed multi-objective techniques are summarised. Moreover, attentions are given to extend the original deterministic problem to a stochastic version. Some robust optimization strategies are also outlined to deal with the stochastic trajectory planning formulation. In addition, a special focus will be given on the recent applications of the optimized trajectory. Finally, some conclusions are drawn and future research on the development of multi-objective and stochastic trajectory optimization techniques is discussed

    Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

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    [EN] Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovacion y Universidades for providing funding through the project RTI2018-096904-B-I00 and the local administration Generalitat Valenciana through projects GV/2017/029 and AICO/2019/055. Franklin Samaniego thanks IFTH (Instituto de Fomento al Talento Humano) Ecuador (2015-AR2Q9209), for its sponsorship of this work.Samaniego, F.; Sanchís Saez, J.; Garcia-Nieto, S.; Simarro Fernández, R. (2020). Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs. Electronics. 9(1):1-23. https://doi.org/10.3390/electronics9010051S12391Kyriakidis, M., Happee, R., & de Winter, J. C. F. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F: Traffic Psychology and Behaviour, 32, 127-140. doi:10.1016/j.trf.2015.04.014Münzer, S., Zimmer, H. D., Schwalm, M., Baus, J., & Aslan, I. (2006). Computer-assisted navigation and the acquisition of route and survey knowledge. Journal of Environmental Psychology, 26(4), 300-308. doi:10.1016/j.jenvp.2006.08.001Morales, Y., Kallakuri, N., Shinozawa, K., Miyashita, T., & Hagita, N. (2013). Human-comfortable navigation for an autonomous robotic wheelchair. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/iros.2013.6696743Krotkov, E., & Hebert, M. (s. f.). Mapping and positioning for a prototype lunar rover. Proceedings of 1995 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1995.525697Rodriguez-Seda, E. J. (2014). Decentralized trajectory tracking with collision avoidance control for teams of unmanned vehicles with constant speed. 2014 American Control Conference. doi:10.1109/acc.2014.6859184Xiaoping Ren, & Zixing Cai. (2010). Kinematics model of unmanned driving vehicle. 2010 8th World Congress on Intelligent Control and Automation. doi:10.1109/wcica.2010.5554512Jun, J.-Y., Saut, J.-P., & Benamar, F. (2016). Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains. Robotics and Autonomous Systems, 75, 325-339. doi:10.1016/j.robot.2015.09.014Li, Y., Ding, L., & Liu, G. (2016). Attitude-based dynamic and kinematic models for wheels of mobile robot on deformable slope. Robotics and Autonomous Systems, 75, 161-175. doi:10.1016/j.robot.2015.10.006Mekonnen, G., Kumar, S., & Pathak, P. M. (2016). Wireless hybrid visual servoing of omnidirectional wheeled mobile robots. Robotics and Autonomous Systems, 75, 450-462. doi:10.1016/j.robot.2015.08.008Xu, J., Wang, M., & Qiao, L. (2015). Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles. Ocean Engineering, 105, 54-63. doi:10.1016/j.oceaneng.2015.06.022Gafurov, S. A., & Klochkov, E. V. (2015). Autonomous Unmanned Underwater Vehicles Development Tendencies. Procedia Engineering, 106, 141-148. doi:10.1016/j.proeng.2015.06.017Qi, X., & Cai, Z. (2018). Three-dimensional formation control based on nonlinear small gain method for multiple underactuated underwater vehicles. Ocean Engineering, 151, 105-114. doi:10.1016/j.oceaneng.2018.01.032Ramasamy, S., Sabatini, R., Gardi, A., & Liu, J. (2016). LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid. Aerospace Science and Technology, 55, 344-358. doi:10.1016/j.ast.2016.05.020Zhu, L., Cheng, X., & Yuan, F.-G. (2016). A 3D collision avoidance strategy for UAV with physical constraints. Measurement, 77, 40-49. doi:10.1016/j.measurement.2015.09.006Chee, K. Y., & Zhong, Z. W. (2013). Control, navigation and collision avoidance for an unmanned aerial vehicle. Sensors and Actuators A: Physical, 190, 66-76. doi:10.1016/j.sna.2012.11.017Courbon, J., Mezouar, Y., Guénard, N., & Martinet, P. (2010). Vision-based navigation of unmanned aerial vehicles. Control Engineering Practice, 18(7), 789-799. doi:10.1016/j.conengprac.2010.03.004Aguilar, W., & Morales, S. (2016). 3D Environment Mapping Using the Kinect V2 and Path Planning Based on RRT Algorithms. Electronics, 5(4), 70. doi:10.3390/electronics5040070Yan, F., Liu, Y.-S., & Xiao, J.-Z. (2013). Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method. International Journal of Automation and Computing, 10(6), 525-533. doi:10.1007/s11633-013-0750-9Yeh, H.-Y., Thomas, S., Eppstein, D., & Amato, N. M. (2012). UOBPRM: A uniformly distributed obstacle-based PRM. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/iros.2012.6385875Liang, Y., & Xu, L. (2009). Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC ’09. doi:10.1145/1543834.1543875Liu, J., Yang, J., Liu, H., Tian, X., & Gao, M. (2016). An improved ant colony algorithm for robot path planning. Soft Computing, 21(19), 5829-5839. doi:10.1007/s00500-016-2161-7Cao, H., Sun, S., Zhang, K., & Tang, Z. (2016). Visualized trajectory planning of flexible redundant robotic arm using a novel hybrid algorithm. Optik, 127(20), 9974-9983. doi:10.1016/j.ijleo.2016.07.078Duan, H., & Qiao, P. (2014). Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing and Cybernetics, 7(1), 24-37. doi:10.1108/ijicc-02-2014-0005Pandey, A., & Parhi, D. R. (2017). Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm. Defence Technology, 13(1), 47-58. doi:10.1016/j.dt.2017.01.001Samaniego, F., Sanchis, J., García-Nieto, S., & Simarro, R. (2019). Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs. Electronics, 8(3), 306. doi:10.3390/electronics8030306Dubins, L. E. (1957). On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents. American Journal of Mathematics, 79(3), 497. doi:10.2307/2372560Fleury, S., Soueres, P., Laumond, J.-P., & Chatila, R. (1995). Primitives for smoothing mobile robot trajectories. IEEE Transactions on Robotics and Automation, 11(3), 441-448. doi:10.1109/70.388788Vanegas, G., Samaniego, F., Girbes, V., Armesto, L., & Garcia-Nieto, S. (2018). Smooth 3D path planning for non-holonomic UAVs. 2018 7th International Conference on Systems and Control (ICSC). doi:10.1109/icosc.2018.8587835Brezak, M., & Petrovic, I. (2014). Real-time Approximation of Clothoids With Bounded Error for Path Planning Applications. IEEE Transactions on Robotics, 30(2), 507-515. doi:10.1109/tro.2013.2283928Barsky, B. A., & DeRose, T. D. (1989). Geometric continuity of parametric curves: three equivalent characterizations. IEEE Computer Graphics and Applications, 9(6), 60-69. doi:10.1109/38.41470Kim, H., Kim, D., Shin, J.-U., Kim, H., & Myung, H. (2014). Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Engineering, 84, 37-44. doi:10.1016/j.oceaneng.2014.03.034Isaacs, J., & Hespanha, J. (2013). Dubins Traveling Salesman Problem with Neighborhoods: A Graph-Based Approach. Algorithms, 6(1), 84-99. doi:10.3390/a6010084Masehian, E., & Kakahaji, H. (2014). NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments. Robotica, 32(7), 1101-1123. doi:10.1017/s0263574713001276Fraichard, T., & Scheuer, A. (2004). From Reeds and Shepp’s to Continuous-Curvature Paths. IEEE Transactions on Robotics, 20(6), 1025-1035. doi:10.1109/tro.2004.833789Pepy, R., Lambert, A., & Mounier, H. (s. f.). Path Planning using a Dynamic Vehicle Model. 2006 2nd International Conference on Information & Communication Technologies. doi:10.1109/ictta.2006.1684472Girbés, V., Vanegas, G., & Armesto, L. (2019). Clothoid-Based Three-Dimensional Curve for Attitude Planning. Journal of Guidance, Control, and Dynamics, 42(8), 1886-1898. doi:10.2514/1.g003551De Lorenzis, L., Wriggers, P., & Hughes, T. J. R. (2014). Isogeometric contact: a review. GAMM-Mitteilungen, 37(1), 85-123. doi:10.1002/gamm.201410005Pigounakis, K. G., Sapidis, N. S., & Kaklis, P. D. (1996). Fairing Spatial B-Spline Curves. Journal of Ship Research, 40(04), 351-367. doi:10.5957/jsr.1996.40.4.351Pérez, L. H., Aguilar, M. C. M., Sánchez, N. M., & Montesinos, A. F. (2018). Path Planning Based on Parametric Curves. Advanced Path Planning for Mobile Entities. doi:10.5772/intechopen.72574Huh, U.-Y., & Chang, S.-R. (2014). A G2 Continuous Path-smoothing Algorithm Using Modified Quadratic Polynomial Interpolation. International Journal of Advanced Robotic Systems, 11(2), 25. doi:10.5772/57340Chang, S.-R., & Huh, U.-Y. (2014). A Collision-Free G2 Continuous Path-Smoothing Algorithm Using Quadratic Polynomial Interpolation. International Journal of Advanced Robotic Systems, 11(12), 194. doi:10.5772/59463Yaochu Jin, & Sendhoff, B. (2008). Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(3), 397-415. doi:10.1109/tsmcc.2008.919172Velasco-Carrau, J., García-Nieto, S., Salcedo, J. V., & Bishop, R. H. (2016). Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification. Journal of Guidance, Control, and Dynamics, 39(2), 372-389. doi:10.2514/1.g001294Honig, E., Schucking, E. L., & Vishveshwara, C. V. (1974). Motion of charged particles in homogeneous electromagnetic fields. Journal of Mathematical Physics, 15(6), 774-781. doi:10.1063/1.1666728Iyer, B. R., & Vishveshwara, C. V. (1988). The Frenet-Serret formalism and black holes in higher dimensions. Classical and Quantum Gravity, 5(7), 961-970. doi:10.1088/0264-9381/5/7/005Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.01

    Bayesian Search Under Dynamic Disaster Scenarios

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    Search and Rescue (SAR) is a hard decision making context where there is available a limited amount of resources that should be strategically allocated over the search region in order to find missing people opportunely. In this thesis, we consider those SAR scenarios where the search region is being affected by some type of dynamic threat such as a wilder or a hurricane. In spite of the large amount of SAR missions that consistently take place under these circumstances, and being Search Theory a research area dating back from more than a half century, to the best of our knowledge, this kind of search problem has not being considered in any previous research. Here we propose a bi-objective mathematical optimization model and three solution methods for the problem: (1) Epsilon-constraint; (2) Lexicographic; and (3) Ant Colony based heuristic. One of the objectives of our model pursues the allocation of resources in riskiest zones. This objective attempts to find victims located at the closest regions to the threat, presenting a high risk of being reached by the disaster. In contrast, the second objective is oriented to allocate resources in regions where it is more likely to find the victim. Furthermore, we implemented a receding horizon approach oriented to provide our planning methodology with the ability to adapt to disaster's behavior based on updated information gathered during the mission. All our products were validated through computational experiments.MaestríaMagister en Ingeniería Industria

    Several approaches for the traveling salesman problem

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    We characterize both approaches, mldp and k-mldp, with several methodologies; both a linear and a non-linear mathematical formulation are proposed. Additionally, the design and implementation of an exact methodology to solve both linear formulations is implemented and with it we obtained exact results. Due to the large computation time these formulations take to be solved with the exact methodology proposed, we analyse the complexity each of these approaches and show that both problems are NP-hard. As both problems are NP-hard, we propose three metaheuristic methods to obtain solutions in shorter computation time. Our solution methods are population based metaheuristics which exploit the structure of both problems and give good quality solutions by introducing novel local search procedures which are able to explore more efficiently their search space and to obtain good quality solutions in shorter computation time. Our main contribution is the study and characterization of a bi-objective problematic involving the minimization of two objectives: an economic one which aims to minimize the total travel distance, and a service-quality objective which aims to minimize of the waiting time of the clients to be visited. With this combination of objectives, we aim to characterize the inclusion of the client in the decision-making process to introduce service-quality decisions alongside a classic routing objective.This doctoral dissertation studies and characterizes of a combination of objectives with several logistic applications. This combination aims to pursue not only a company benefit but a benefit to the clients waiting to obtain a service or a product. In classic routing theory, an economic approach is widely studied: the minimization of traveled distance and cost spent to perform the visiting is an economic objective. This dissertation aims to the inclusion of the client in the decision-making process to bring out a certain level of satisfaction in the client set when performing an action. We part from having a set of clients demanding a service to a certain company. Several assumptions are made: when visiting a client, an agent must leave from a known depot and come back to it at the end of the tour assigned to it. All travel times among the clients and the depot are known, as well as all service times on each client. This is to say, the agent knows how long it will take to reach a client and to perform the requested service in the client location. The company is interested in improving two characteristics: an economic objective as well as a servicequality objective by minimizing the total travel distance of the agent while also minimizing the total waiting time of the clients. We study two main approaches: the first one is to fulfill the visits assuming there is a single uncapacitated vehicle, this is to say that such vehicle has infinite capacity to attend all clients. The second one is to fulfill the visits with a fleet of k-uncapacitated vehicles, all of them restricted to an strict constraint of being active and having at least one client to visit. We denominate the single-vehicle approach the minimum latency-distance problem (mldp), and the k-sized fleet the k-minimum latency-distance problem (k-mldp). As previously stated, this company has two options: to fulfil the visits with a single-vehicle or with a fixed-size fleet of k agents to perform the visits

    The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

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    This work introduces a new routing problem called the Dynamic Multi-Objective Multi-vehicle Covering Tour Problem (DMOMCTP). The DMOMCTPs is a combinatorial optimization problem that represents the problem of routing multiple vehicles to survey an area in which unpredictable target nodes may appear during execution. The formulation includes multiple objectives that include minimizing the cost of the combined tour cost, minimizing the longest tour cost, minimizing the distance to nodes to be covered and maximizing the distance to hazardous nodes. This study adapts several existing algorithms to the problem with several operator and solution encoding variations. The efficacy of this set of solvers is measured against six problem instances created from existing Traveling Salesman Problem instances which represent several real countries. The results indicate that repair operators, variable length solution encodings and variable-length operators obtain a better approximation of the true Pareto front

    Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification

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    In this paper, a novel method for aerodynamic model identification of a micro-air vehicle is proposed. The principal contribution is a technique of wind estimation that provides information about the existing wind during flight when no air-data sensors are available. The estimation technique employs multi-objective optimization algorithms that utilize identification errors to propose the wind-speed components that best fit the dynamic behavior observed. Once the wind speed is estimated, the flight experimentation data are corrected and utilized to perform an identification of the aircraft model parameters. A multi-objective optimization algorithm is also used, but with the objective of estimating the aerodynamic stability and control derivatives. Employing data from different flights offers the possibility of obtaining sets of models that form the Pareto fronts. Deciding which model best adjusts to the experiments performed (compromise model) will be the ultimate task of the control engineer.The authors would like to thank the Spanish Ministry of Innovation and Science for providing funding through grant BES-2012-056210 and projects TIN-2011-28082 and ENE-25900. We also want to acknowledge the Generalitat Valenciana for financing this work through project PROMETEO/2012/028.Velasco Carrau, J.; García-Nieto Rodríguez, S.; Salcedo Romero De Ávila, JV.; Bishop, RH. (2015). Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification. Journal of Guidance, Control, and Dynamics. 39(2):372-389. https://doi.org/10.2514/1.G001294S37238939

    Multi-objective heuristics applied to robot task planning for inspection plants

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    Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by presenting experimental results obtained over realistic scenarios of two heuristic solvers (MOHS and NSGA-II) aimed at efficiently scheduling tasks in robotic swarms that collaborate together to accomplish a mission. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks whereas the relative execution order of such tasks within the schedule of certain robots is computed based on the Traveling Salesman Problem (TSP). Experimental results in three different deployment scenarios reveal the goodness of the proposed technique based on the Multi-objective Harmony Search algorithm (MOHS) in terms of Hypervolume (HV) and Coverage Rate (CR) performance indicators
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