Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions.

High-flying unmanned reconnaissance and surveillance systems are now being used extensively in the United States military. Current development programs are producing demonstrations of next-generation unmanned flight systems that are designed to perform combat missions. Their use in first-strike combat operations will dictate operations in densely cluttered environments that include unknown obstacles and threats, and will require the use of terrain for masking. The demand for autonomy of operations in such environments dictates the need for advanced trajectory optimization capabilities. In addition, the ability to coordinate the movements of more than one aircraft in the same area is an emerging challenge. This thesis examines using an analytical reduced order formulation for trajectory generation for minimum time and terrain masking cases. First, pseudo-3D constant velocity equations of motion are used for path planning for a single vehicle. In addition, the inclusion of winds, moving targets and moving threats is considered. Then, this formulation is increased to using 3D equations of motion, both with a constant velocity and with a simplified varying velocity model. Next, the constant velocity equations of motion are expanded to include the simultaneous path planning of an unspecified number of vehicles, for both aircraft avoidance situations and formation flight cases.

The air traffic system in the United States is currently undergoing a complete overhaul known as "NextGen". NextGen is the FAA's initiative to update the antiquated National Airspace System (NAS) both procedurally and technologically to reduce costs to the users and negative impacts on the general public. There are currently numerous studies being conducted that are focused on finding optimal solutions to the problems of congestion, delay, and the high fuel and noise footprints associated aircraft operations. These studies require accurate simulation techniques to assess the potential benefits and drawbacks for new procedures and technology. One common method uses air traffic control radar data. As an aircraft travels through the air traffic control system, its latitude, longitude, and altitude are recorded at set intervals. From these values, estimates of groundspeed and heading can be derived. Researchers then use this data to estimate aircraft performance parameters such as engine thrust and aircraft configuration, variables essential to estimate fuel burn, noise, and emissions. This thesis creates a more accurate method of simulating aircraft performance based solely on air traffic control radar data during the arrival process. This tool will allow the benefits of different arrival procedures to be compared at a variety of airports and wind conditions before costly flight testing is required. The accuracy of the performance estimates will be increased using the Tool for Assessing Separation and Throughput (TASAT), a fast-time Monte Carlo aircraft simulator that can simulate multiple arrivals with a mixture of different aircraft types. The tool has succeeded in matching various recorded radar profiles and has produced fuel burn estimates with an RMS error of less than 200 pounds from top of descent to landing when compared to high fidelity operational data. The output from TASAT can also be ported to FAA software tools to make higher quality predictions of aircraft noise and emissions.

The word 'rendezvous' implies a pre-arranged meeting between two entities for a specific purpose. This term is used in the study of spacecraft operations, to describe a set of maneuvers performed by two spacecraft in order to achieve a match in position and velocity. The term 'orbital rendezvous' applies to rendezvous between spacecraft in earth-centered orbits. Considering its obvious scope for application in the assembly, maintenance and retrieval of earth satellites, the importance of orbital rendezvous towards maintaining a sustained presence in space can be easily appreciated. This particular study deals with the development of a guidance scheme for an orbital rendezvous operation, wherein only one of the spacecraft, called the chaser, is assumed to be provided with a capability to maneuver, while the other spacecraft, the target, is assumed to be thrust-free or passive. There is presently a lot of interest in autonomous trajectory planning and guidance schemes for orbital rendezvous missions. Autonomy here, refers to the absence of ground supervision and control over the on-board planning and guidance process, and is expected to result in greater mission flexibility and lower operating costs. The terms trajectory planning and guidance collectively refer to the optimization process used to determine minimum-fuel trajectories, and the means employed to make the spacecraft follow them, based on navigational updates. The challenge lies mainly in making the autonomous scheme real-time implementable, and at the same time compatible with the limited computational capabilities available on-board. It is well known that a large part of the computation times and costs, when determining optimal trajectories, are taken up by (1) the prediction of spacecraft motion using numerical integration schemes, and (2) the use of iterative numerical techniques to solve the non-linear, coupled system of equations obtained as boundary conditions in the trajectory optimization problem. There exists on the other hand, a wealth of results from analytical investigations into the motion of spacecraft, that can be profitably utilized by use of suitable assumptions, to reduce computation times and costs relating to trajectory prediction. The present thesis seeks to follow this course, while trying to ensure that the assumptions made do not influence in a negative manner the accuracy of the guidance scheme. The assumptions to be described below are based on the division of the total rendezvous maneuver into sub-phases. The trajectory optimization problems for the individual sub-phases are first considered independent of one another. A method is then found to combine the two sub-phases in an optimal manner. The initial or the homing phase of the rendezvous maneuver, consists of an open-loop orbit transfer, intended to place the chaser within a 'window of proximity' spanning a few hundreds of kilometers, of the target. In order to avoid time consuming numerical integration of the non-homogeneous, non-linear central force-field equations of motion, an impulsive thrust model is assumed. A parametric optimization method is used to determine the location, orientation and magnitude of the impulses for a minimum-fuel rendezvous transfer, as it is well known that parametric optimization methods are robust compared to the more general functional optimization methods. A two-impulse transfer is selected, knowing that at least two-impulses are required for a rendezvous maneuver, and that methods are available if necessary, to obtain optimal multi-impulse trajectories from a two-impulse solution. The total characteristic velocity, a scalar cost function related to fuel-consumption, is minimized with respect to a set of independent variables. The variables chosen in this case to determine the rendezvous transfer are (1) the transfer angle θc defining an initial coast in the chaser orbit C by the chaser, (2) the transfer angle θs defining a coast by the target to the position of the second impulse in the target orbit S and (3) a parameter (say p ) that determines the shape of the transfer orbit T between the first and second impulses.

<p>There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast. </p><p>This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.</p>

Thesis (Ph. D.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2004. / Includes bibliographical references (p. 129-141).

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

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).