Two-stage trajectory optimization for autonomous ground vehicles parking maneuver

This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multi-layer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework

A review of optimization techniques in spacecraft flight trajectory design

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

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Nonlinear mixed integer based optimization technique for space applications

In this thesis a new algorithm for mixed integer nonlinear programming (MINLP) is developed and applied to several real world applications with special focus on space applications. The algorithm is based on two main components, which are an extension of the Ant Colony Optimization metaheuristic and the Oracle Penalty Method for constraint handling. A sophisticated implementation (named MIDACO) of the algorithm is used to numerically demonstrate the usefulness and performance capabilities of the here developed novel approach on MINLP. An extensive amount of numerical results on both, comprehensive sets of benchmark problems (with up to 100 test instances) and several real world applications, are presented and compared to results obtained by concurrent methods. It can be shown, that the here developed approach is not only fully competitive with established MINLP algorithms, but is even able to outperform those regarding global optimization capabilities and cpu runtime performance. Furthermore, the algorithm is able to solve challenging space applications, that are considered here as mixed integer problems for the very first time

Mission Planning Application Software for Solar Powered UAVs

The growing demand for unmanned aerial vehicles (UAV) for dedicated civilian use over the last decade has attracted the attention of investigators and engineers all over the world. It is important to note that the non-necessity of manual piloting is ideally suited to the operation of dirty, dangerous, dull (long autonomy) or large scale missions (use of swarms of UAVs) [1], however it demands a greater level of attention to the development of technologies that allow and ease the planning, operation and management of such vehicles. A lot of improvement has been made in the development of solar-powered UAVs, which promise a low-energy cost, silent and clean operation. However, despite solar energy being free and abundant, among many the present cost, complexity, solar energy capture systems’ efficiency, electric storage and traction efficiency, as well as the consequent requirement for large-size vehicles, greatly restricts the extensive use of these UAVs [2], besides the added difficulties from the absence of a human pilot. Nevertheless, the present work covers the development of a graphical user interface (GUI) associated to the improvement of a mission planning software created by past work, allying flexibility and quickness to the planning efficiency of solar UAV operations. Beyond facilitating the input of necessary data to the optimization of a pre-set route, this interface allows to export the optimized route to the open-source ground control station (GCS) program “MissionPlanner” (MP) [3]. In addition, as part of an exhaustive testing process, the final ensembled software was run several times, proving its capabilities and limitations in a real operational situation.A crescente procura por veículos aéreos não tripulados (UAV) para uso civil na última década tem atraído a atenção de investigadores e engenheiros um pouco por todo o mundo. É importante realçar que a sua desnecessidade de pilotagem manual é idealmente adequada à realização de missões “sujas”, perigosas, monótonas (longa autonomia) ou de grande escala (uso de “enxames” de UAVs) [1], contudo exige uma maior atenção ao desenvolvimento de tecnologias que permitam e facilitem o planeamento, operação e gestão destes veículos. Bastantes avanços têm sido feitos em UAVs movidos a energia solar, que prometem uma operação de baixo custo energético, silenciosa e limpa. Contudo, por mais que a energia solar seja livre e abundante, o presente custo, complexidade, eficiência dos sistemas de captação solar, do armazenamento e da tração usando energia elétrica, bem como a consequente necessidade de veículos de grande tamanho, restringe muito a aplicação extensiva destes veículos [2], para além das dificuldades acrescidas pela ausência de um piloto humano. Não obstante, esta dissertação abrange o desenvolvimento de um interface gráfico de utilizador (GUI) associado ao aperfeiçoamento de um software de planeamento de missões criado a partir de projetos passados, aliando a flexibilidade e rapidez à eficiência de planeamento da operação de UAVs solares. Para além de facilitar a introdução de dados necessários à otimização de uma rota predefinida, este interface permite exportar a rota otimizada para o programa open-source de estação de controlo de solo (GCS) “MissionPlanner” (MP) [3]. Para além disso, o software conjunto final foi também executado como parte de um teste exaustivo, provando as suas capacidades e limitações numa situação real de operação

Coordinated and optimized voltage management of distribution networks with multi-microgrids

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles

Management: A bibliography for NASA managers

This bibliography lists 630 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1991. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy