Navigation is an invisible utility that is often taken for granted with considerable societal and economic impacts. Not only is navigation essential to our modern life, but the more it advances, the more possibilities are created. Navigation is at the heart of three emerging fields: autonomous vehicles, location-based services, and intelligent transportation systems. Global navigation satellite systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). The conventional approach to overcome the limitations of GNSS-based navigation is to couple GNSS receivers with dead reckoning sensors. A new paradigm, termed opportunistic navigation (OpNav), is emerging. OpNav is analogous to how living creatures naturally navigate: by learning their environment. OpNav aims to exploit the plenitude of ambient radio frequency signals of opportunity (SOPs) in the environment. OpNav radio receivers, which may be handheld or vehicle-mounted, continuously search for opportune signals from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative opportunistic navigation (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape. For the sake of motivation, consider the following problem. A number of receivers with no a priori knowledge about their own states are dropped in an environment comprising multiple unknown terrestrial SOPs. The receivers draw pseudorange observations from the SOPs. The receivers' objective is to build a high-fidelity signal landscape map of the environment within which they localize themselves in space and time. We then ask: (i) Under what conditions is the environment fully observable? (ii) In cases where the environment is not fully observable, what are the observable states? (iii) How would receiver-controlled maneuvers affect observability? (iv) What is the degree of observability of the various states in the environment? (v) What motion planning strategy should the receivers employ for optimal information gathering? (vi) How effective are receding horizon strategies over greedy for receiver trajectory optimization, and what are their limitations? (vii) What level of collaboration between the receivers achieves a minimal price of anarchy? This dissertation addresses these fundamental questions and validates the theoretical conclusions numerically and experimentally. / text

An analysis of the use of complex-step differentiation (CSD) in optimization problems is presented. Complex-step differentiation is a numerical approximation of the derivative of a function valid for any real-valued analytic function. The primary benefit of this method is that the approximation does not depend on a difference term; therefore round-off error is reduced to the machine word-length. A suitably small choice of the perturbation length, h, then results in the virtual elimination of truncation error in the series approximation. The theoretical basis for this method is derived highlighting its merits and limitations. The Lunar Ascent Problem is used to compare CSD to traditional forward differencing in applications useful to the solution of optimization problems. Complex-step derivatives are shown to sufficiently apply in various interpolation and integration methods, and, in fact, consistently outperform traditional methods. Further, the Optimal Orbit Transfer Problem is used to test the accuracy, robustness, and runtime of CSD in comparison to central differencing. It is shown that CSD is a considerably more accurate derivative approximation which results in an increased robustness and decreased optimization time. Also, it is shown that each approximation is computed in less time using CSD than central differences. Overall, complex-step derivatives are shown to be a fast, accurate, and easy to implement differentiation method ideally suited for most optimization problems. / text

This dissertation presents the research, development and application of trajectory creation, obstacle avoidance and trajectory optimisation methods for an existing serial manipulator power line inspection robot (PLIR). The obstacle avoidance implementation allows the robot to navigate around an obstacle obstructing its navigation along the line. The algorithm generated end effector trajectory waypoints autonomously based on bounding box obstacle descriptions in Cartesian space, and connected them with a fifth order basis-spline end effector trajectory command. The trajectories were created taking into account the dynamic torque and velocity constraints of the robot while ignoring non-linearities. Performance was inspected and evaluated in a simulated workspace environment. The trajectory optimisation was designed to maximise the robot’s operating range, with constraints on the battery power supply, by minimising charge consumed during obstacle avoidance trajectories. The temporal components of the basis-spline trajectories were optimised by minimising a timeenergy type of cost function subject to the dynamic constraints of the robot. Cost function analyses are presented for a simple frictionless robot model based on the recursive Newton-Euler method, and for a more realistic model including viscous, Coulomb and static friction as well as gearbox backlash. It is shown that the Nelder-Mead simplex method was appropriate for optimisation. For representative trajectories that were studied, the optimiser was capable of finding global minima with satisfactory speed and accuracy in simulation. The validity of trajectory optimisation with regard to the cost function behaviour was confirmed. This was based on experiments carried out on the robot hardware in the laboratory, examining the predicted and actual actuator current profiles. The engineering design and implementation of hardware and software for the base station and on-board system is presented, together with the layout of the PLIR’s control system and PID (proportional-integral-derivative) controller design. Trajectory commands are sent from the base station to the robot via Wi-Fi for execution. Furthermore, live video feed from the robot can be sent to the ground station computer. Furthermore, high voltage testing of the PLIR showed that the engineering design of the robot and communication platform is robust. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.

Trajectory optimization is an important part of launch vehicle design and operation. With the high costs of launching payload into orbit, every pound that can be saved increases affordability. One way to save weight in launch vehicle design and operation is by optimizing the ascent trajectory. Launch vehicle trajectory optimization is a field that has been studied since the 1950’s. Originally, analytic solutions were sought because computers were slow and inefficient. With the advent of computers, however, different algorithms were developed for the purpose of trajectory optimization. Computer resources were still limited, and as such the algorithms were limited to local optimization methods, which can get stuck in specific regions of the design space. Local methods for trajectory optimization have been well studied and developed. Computer technology continues to advance, and in recent years global optimization has become available for application to a wide variety of problems, including trajectory optimization. The aim of this thesis is to create a methodology that applies global optimization to the trajectory optimization problem. Using information from a global search, the optimization design space can be reduced and a much smaller design space can be analyzed using already existing local methods. This allows for areas of interest in the design space to be identified and further studied and helps overcome the fact that many local methods can get stuck in local optima. The design space included in trajectory optimization is also considered in this thesis. The typical optimization variables are initial conditions and flight control variables. For direct optimization methods, the trajectory phase structure is currently chosen a priori. Including trajectory phase structure variables in the optimization process can yield better solutions. The methodology and phase structure optimization is demonstrated using an earth-to-orbit trajectory of a Delta IV Medium launch vehicle. Different methods of performing the global search and reducing the design space are compared. Local optimization is performed using the industry standard trajectory optimization tool POST. Finally, methods for varying the trajectory phase structure are presented and the results are compared.

Modeling, simulation, and optimization play vital roles throughout the engineering design process; however, in many design disciplines the cost of simulation is high, and designers are faced with a tradeoff between the number of alternatives that can be evaluated and the accuracy with which they can be evaluated. In this thesis, a methodology is presented for using models of various levels of fidelity during the optimization process. The intent is to use inexpensive, low-fidelity models with limited accuracy to recognize poor design alternatives and reserve the high-fidelity, accurate, but also expensive models only to characterize the best alternatives. Specifically, by setting a user-defined performance threshold, the optimizer can explore the design space using a low-fidelity model by default, and switch to a higher fidelity model only if the performance threshold is attained. In this manner, the high fidelity model is used only to discern the best solution from the set of good solutions, so that computational resources are conserved until the optimizer is close to the solution. This makes the optimization process more efficient without sacrificing the quality of the solution. The method is illustrated by optimizing the trajectory of a hydraulic backhoe. To characterize the robustness and efficiency of the method, a design space exploration is performed using both the low and high fidelity models, and the optimization problem is solved multiple times using the variable fidelity framework.

Thesis (M.S.)--Ohio University, March, 2004. / Title from PDF t.p. Includes bibliographical references (leaves 80-81).

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (p. 87-91).

Le développement de comportements utiles pour les robots complexes, tel que des humanoïdes, s'avère difficile. La commande corps-complet à base de modèle allège en partie ces difficultés, en permettant la composition des comportements corps-complets complexes à partir de plusieurs tâches atomiques effectuées simultanément sur le robot. Cependant, des hypothèses et erreurs de modélisation, faites pendant la planification des tâches, peuvent produire des combinaisons infaisables/incompatibles quand exécutées sur le robot, créant des mouvements corps-complet imprévisibles, et probablement dangereux. L'objectif de ce travail est de mieux comprendre ce qui rend les tâches infaisables ou incompatibles et de développer des méthodes automatiques pour améliorer ces problèmes pour que les mouvements corps-complets puissent être accomplis comme prévu. Nous commençons par construire un formalisme permettant d'analyser quand les tâches sont faisables et compatibles étant données les contraintes de commande. En utilisant les métriques de faisabilité et compatibilité à base de modèle, nous démontrons comment optimiser les tâches avec des outils de commande prédictive non-linéaire ainsi que les inconvénients de cette approche. Afin de surmonter ces faiblesses, une boucle d'optimisation est formulée, qui améliore automatiquement la faisabilité et compatibilité des tâches via la recherche de politique sans modèle en conjonction avec la commande corps-complets à base de modèle. À travers une série d'expériences simulées et réelles, nous montrons que la simple optimisation de faisabilité et compatibilité des tâches nous permet de réaliser des mouvements corps-complets utiles. / Producing useful behaviors on complex robots, such as humanoids, is a challenging undertaking. Model-based whole-body control alleviates some of this difficulty by allowing complex whole-body motions to be broken up into multiple atomic tasks, which are performed simultaneously on the robot. However, modeling errors and assumptions, made during task planning, often result in infeasible and/or incompatible task combinations when executed on the robot. Consequently, there is no guarantee that the prescribed tasks will be accomplished, resulting in unpredictable, and most likely, unsafe whole-body motions. The objective of this work is to better understand what makes tasks infeasible or incompatible, and develop automatic methods of improving on these two issues so that the overall whole-body motions may be accomplished as planned. We start by building a concrete analytical formalism of what it means for tasks to be feasible with the control constraints and compatible with one another. Using the model-based feasibility and compatibility metrics, we demonstrate how the tasks can be optimized using non-linear model predictive control, while also detailing the shortcomings of this model-based approach. In order to overcome these weaknesses, an optimization loop is designed, which automatically improves task feasibility and compatibility using model-free policy search in conjunction with model-based whole-body control. Through a series of simulated and real-world experiments, we demonstrate that by simply optimizing the tasks to improve both feasibility and compatibility, complex and useful whole-body motions can be realized.

The aim of diploma theses is the design of an adaptive robotic workplace. The theoretical part focus on the division of robotic systems and the technical description of individual devices that were used in the implementation of the solution. The practical part contains an analysis of solutions and optimization of the entire production process in terms of minimizing the trajectory, smoothness of movements, time interval, which were analyzed in RoboSim software and in Roboshop software source code was created. Part of the theses is the design for an adaptive production process. The result of the work is an algorithm for controlling robot movements between individual processes. The theses contain a variant solution and possible innovative solutions for possible expansion of the workplace.

In this work, capturing Near-Earth Asteroids (NEAs) into Near-Earth orbits is investigated. A general optimization strategy is employed whereby a genetic algorithm is used to seed a sequential quadratic programming (SQP) method for the first step, and then nearby solutions seed further SQP runs. A large number of solutions are produced for several asteroids with varying levels of thrust, and under the effects of various perturbations. Solutions are found over a range of epochs and times of flight as opposed to many traditional methods of optimizing point solutions. This methodology proved effective, finding low-thrust capture solutions within 10% of the required Delta V for analytically estimated transfers, and matching results from other optimization programs such as MALTO. It is found that the effects of solar radiation pressure (SRP) and n-body effects do not have a significant impact on the optimized transfer costs, nor do the perturbations significantly affect the shapes and trends of the optimized solution space. <p><br></p> <p>These optimized results are then used to develop analytic models for both optimized transfer costs and flight times. These models are then used to estimate the transfer costs and flight times for all listed Near Earth Asteroids from the JPL Small Body Database. This analysis is then used to determine the nominal properties of potentially capturable asteroids. The characteristics are then related to a series of different asteroid transfer technologies, elucidating each technology's capabilities and potential capture targets. Finally, this analysis concludes with a brief roadmap of the major decisions mission designers should consider for future asteroid capture missions.</p>