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Daniel Guggenheim School of Aerospace Engineering

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search.filters.applied.f.advisor: Tsiotras, Panagiotis × End date: 2022 × Start date: 2020 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Engineering × search.filters.applied.f.resourcesubtype: Dissertation ×

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Now showing 1 - 6 of 6
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Generalized Heuristic Search Algorithms with Applications to Motion Planning and Multi-Agent Path Finding Problems

2022-08-01 , Lim, Jaein

This thesis investigates novel ways of leveraging generalized interpretations of heuristics to solve complex motion planning problems with completeness and bounded suboptimality guarantees. A set of heuristic search algorithms is developed to utilize relaxed notions of relevancy to more efficiently solve path planning, motion planning and multi-agent path finding problems. The main focus of this thesis is to demonstrate how using generalized heuristics based on the relaxed notions of relevancy helps the hereto developed search algorithms focus their computational efforts to make better progress towards finding a solution. The theoretical properties of the developed algorithms are extensively studied, and their numerical performances are benchmarked against state-of-the-art algorithms across various robotic platforms. This thesis proceeds with a brief introduction and background on existing heuristic search algorithms and their limitations in solving real world planning problems, delineating our contributions in Chapter 1. The main contributions of this thesis follow in the consequent four chapters, where four distinct planning frameworks are presented: hierarchically abstracted path planning, lazy replanning, colored planning, and multi-agent path finding. Each framework is dealt in greater detail in each of the four consequent chapters. Chapter 2 considers planning on hierarchically abstracted graphs by utilizing distributed abstract information as heuristics to find a globally refined solution. Chapter 3 considers lazy replanning which utilizes previous search results as heuristics to facilitate a new plan, while delaying expensive edge evaluations. Chapter 4 considers using semantic information as heuristics to guide search in a principled way. Finally, the multi-agent path finding problem is considered in Chapter 5, namely, the problem of finding a set of collision-free paths for a team of agents while minimizing some global cost, focusing on how the ideas presented in the preceding chapters help produce an efficient algorithm. The thesis is concluded in Chapter 6 with a discussion on potential future research directions.

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Applied Stochastic Optimal Control for Spacecraft Guidance

2021-04-30 , Ridderhof, Jack

Optimal control theory has been successfully applied to a wide range of a problems in spacecraft trajectory optimization. Historically, the identification and management of uncertainty in spaceflight applications has been a separate endeavor from optimal trajectory design, with the exception of heuristic margins applied on the deterministic optimal trajectory. Following a stochastic optimal control approach, on the other hand, leads to the direct consideration of uncertainty for the design of closed-loop trajectories with probabilistic constraints. Resulting control laws are designed with respect to all possible trajectory and control input realizations, and the performance is evaluated over measures of the aggregate, or expected, state and control trajectories. This dissertation focuses on specific applications of stochastic optimal control for spacecraft guidance, namely: powered descent guidance (PDG), atmospheric entry guidance, and aerocapture guidance. In addition, extensions are developed, which have further applications for spacecraft guidance, to the general theory of applying convex optimization to jointly steer the mean and covariance of stochastic systems, subject to probabilistic constraints. For minimum-fuel PDG, the problem of setting non-conservative thrust margins is addressed by application of minimum-variance, covariance-constrained stochastic optimal control. The resulting closed-loop PDG process does not, with high probability, either saturate thrust commands or deviate too far from the desired landing site. Next, entry guidance in an atmosphere with spatially-dependent random variations in the atmospheric density is posed as a chance-constrained stochastic optimal control problem; the resulting targeting accuracy is shown to be better than the current state-of-the-art Apollo-derived entry guidance. Finally, in order to address the problem of aerocapture guidance around a planet with an unknown atmosphere, a successive convex programming-based method is developed to solve chance-constrained stochastic optimal control problems for systems acting in the presence of a Gaussian random field. In a numerical example of an aerocapture mission with bank angle control, the developed method is used to solve for a control law that explicitly minimizes the 99th percentile of the required Delta-V, subject to constraints on the probability distribution of the closed-loop bank angle during atmospheric flight.

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INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING

2022-05-03 , Joshi, Sagar Suhas

Sampling-based methods have emerged as a promising technique for solving robot motion-planning problems. These algorithms avoid a priori discretization of the search-space by generating random samples and building a graph online. While the recent advances in this area endow these randomized planners with asymptotic optimality, their slow convergence rate still remains a challenge. One of the reasons for this poor performance can be traced to the widely used uniform sampling strategy that na ̈ıvely explores the entire search-space. Having access to an intelligent exploration strategy that can focus search, would alleviate one of the critical bottlenecks in speeding up these algorithms. This thesis endeavors to tackle this problem by presenting exploration algorithms that leverage different sources of information available during planning time.

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Games of Pursuit-Evasion with Multiple Agents and Subject to Uncertainties

2021-04-30 , Makkapati, Venkata Ramana

Over the past decade, there have been constant efforts to induct unmanned aerial vehicles (UAVs) into military engagements, disaster management, weather monitoring, and package delivery, among various other applications. With UAVs starting to come out of controlled environments into real-world scenarios, uncertainties that can be either exogenous or endogenous play an important role in the planning and decision-making aspects of deploying UAVs. At the same time, while the demand for UAVs is steadily increasing, major governments are working on their regulations. There is an urgency to design surveillance and security systems that can efficiently regulate the traffic and usage of these UAVs, especially in secured airspaces. With this motivation, the thesis primarily focuses on airspace security, providing solutions for safe planning under uncertainties while addressing aspects concerning target acquisition and collision avoidance. In this thesis, we first present our work on solutions developed for airspace security that employ multiple agents to capture multiple targets in an efficient manner. Since multi-pursuer multi-evader problems are known to be intractable, heuristics based on the geometry of the game are employed to obtain task-allocation algorithms that are computationally efficient. This is achieved by first analyzing pursuit-evasion problems involving two pursuers and one evader. Using the insights obtained from this analysis, a dynamic allocation algorithm for the pursuers, which is independent of the evader's strategy, is proposed. The algorithm is further extended to solve multi-pursuer multi-evader problems for any number of pursuers and evaders, assuming both sets of agents to be heterogeneous in terms of speed capabilities. Next, we consider stochastic disturbances, analyzing pursuit-evasion problems under stochastic flow fields using forward reachability analysis, and covariance steering. The problem of steering a Gaussian in adversarial scenarios is first analyzed under the framework of general constrained games. The resulting covariance steering problem is solved numerically using iterative techniques. The proposed approach is applied to the missile endgame guidance problem. Subsequently, using the theory of covariance steering, an approach to solve pursuit-evasion problems under external stochastic flow fields is discussed. Assuming a linear feedback control strategy, a chance-constrained covariance game is constructed around the nominal solution of the players. The proposed approach is tested on realistic linear and nonlinear flow fields. Numerical simulations suggest that the pursuer can effectively steer the game towards capture. Finally, the uncertainties are assumed to be parametric in nature. To this end, we first formalize optimal control under parametric uncertainties while introducing sensitivity functions and costates based techniques to address robustness under parametric variations. Utilizing the sensitivity functions, we address the problem of safe path planning in environments containing dynamic obstacles with an uncertain motion model. The sensitivity function based-approach is then extended to address game-theoretic formulations that resemble a "fog of war" situation.

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Planning For Satellite Actuator Failures: A Falsification Approach Towards Certification of Contingency Controllers

2022-03-14 , Brewer, John

Today, more satellites are being launched at a rate never experienced before. This is due, in part, to the miniaturization of the technology and the increasing reliance on smaller satellites which are cheaper to build, launch, and replace compared to the large monolithic satellites of the past. However, these small satellites still possess strikingly high failure rates that are often the result of design issues, the lack of testing, and uncertainties in hardware components. As satellites grow in complexity, incorporate more features, and are built at a faster rate, the ability to design and test successful systems becomes urgent and difficult. This thesis aims to present a falsification approach to the automated verification and validation approach to satellite systems. Specifically, we seek to address how falsification techniques can be used to test and validate contingency plan controllers designed to rescue a satellite in the event an actuator failure occurs. These contingency control schemes are complicated implementations which, not only require unique controllers capable of stabilizing a satellite that has lost controllability, but they must also perform identification of the failure that has occurred and invoke the switching needed from the primary controller to a backup controller most suitable to handle the failure experienced by the satellite. Verifying these types of complex control structures by hand is difficult, so the development and demonstration of an automated framework capable of performing this challenging task would not only be a valuable contribution to the current state of spacecraft technology, but would also lead to a significant reduction in the failure rate of satellites seen in the space community over the past 20 years.

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Autonomous aggressive driving: theory & experiments

2020-01-19 , You, Changxi

Autonomous vehicles represent a major trend in future intelligent transportation systems. In order to develop autonomous vehicles, this dissertation intends to understand expert driving maneuvers in different scenarios such as highway overtaking and off-road rally racing, which are referred to as ``aggressive'' driving in the context of this dissertation. By mimicking expert driving styles, one expects to be able to improve the vehicle's active safety and traffic efficiency in the development of autonomous vehicles. This dissertation starts from the system modeling, namely, driver modeling, vehicle modeling and traffic system modeling, for which we implement different Kalman type filters for nonlinear parameter estimation using experimental data. We then focus on the optimal decision making, path planning and control design problems for highway overtaking and off-road autonomous rally racing, respectively. We propose to use a stochastic MDP for highway traffic modeling. The new concept of ``dynamic cell'' is introduced to dynamically extract the essential state of the traffic according to different vehicle velocities, driver intents (i.e., lane-switching, braking, etc.) and sizes of the surrounding vehicles (i.e., truck, sedan, etc.). This allows us to solve the (inverse) reinforcement learning problem efficiently since the dimensionality of the state space can be maintained in a manageable level. New path planning algorithms using Bezier curves are proposed to generate everywhere 𝐶2 continuous curvature-constrained paths for highway real-time lane-switching. We demonstrate expert overtaking maneuver by implementing the proposed decision making, path planning and control algorithms on an in-house developed traffic simulator. Based on the trajectory learning result, we model high-speed cornering with a segment of steady-state cornering for off-road rally racing. We then propose a geometry-based trajectory planning algorithm using the vehicle's differential flatness. This approach avoids solving optimal control problems on-the-fly, while guaranteeing good racing performance in off-road racing.

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