Bio-inspired, Varying Manifold Based Method With Enhanced Initial Guess Strategies For Single Vehicle\u27s Optimal Trajectory Planning

Trajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species, such as hoverflies, ants, and bats, have developed many efficient hunting strategies. It is hypothesized that these types of predators only move along paths in a carefully selected manifold based on the prey’s motion in some of their hunting activities. Inspired by these studies, the predator-prey relationships are organized into a unified form and incorporated into the trajectory optimization formulation, which can reduce the computational cost in solving nonlinear constrained optimal trajectory planning problems. Specifically, three motion strategies are studied in this dissertation: motion camouflage, constant absolute target direction, and local pursuit. Necessary conditions based on the speed and obstacle avoidance constraints are derived. Strategies to tune initial guesses are proposed based on these necessary conditions to enhance the convergence rate and reduce the computational cost of the motion camouflage inspired strategy. The following simulations have been conducted to show the advantages of the proposed methods: a supersonic aircraft minimum-time-to-climb problem, a ground robot obstacle avoidance problem, and a micro air vehicle minimum time trajectory problem. The results show that the proposed methods can find the optimal solution with higher success rate and faster iv convergent speed as compared with some other popular methods. Among these three motion strategies, the method based on the local pursuit strategy has a relatively higher success rate when compared to the other two. In addition, the optimal trajectory planning method is embedded into a receding horizon framework with unknown parameters updated in each planning horizon using an Extended Kalman Filte

Virtual Motion Camouflage Based Nonlinear Constrained Optimal Trajectory Design Method

Nonlinear constrained optimal trajectory control is an important and fundamental area of research that continues to advance in numerous fields. Many attempts have been made to present new methods that can solve for optimal trajectories more efficiently or to improve the overall performance of existing techniques. This research presents a recently developed bio-inspired method called the Virtual Motion Camouflage (VMC) method that offers a means of quickly finding, within a defined but varying search space, the optimal trajectory that is equal or close to the optimal solution. The research starts with the polynomial-based VMC method, which works within a search space that is defined by a selected and fixed polynomial type virtual prey motion. Next will be presented a means of improving the solution’s optimality by using a sequential based form of VMC, where the search space is adjusted by adjusting the polynomial prey trajectory after a solution is obtained. After the search space is adjusted, an optimization is performed in the new search space to find a solution closer to the global space optimal solution, and further adjustments are made as desired. Finally, a B-spline augmented VMC method is presented, in which a B-spline curve represents the prey motion and will allow the search space to be optimized together with the solution trajectory. It is shown that (1) the polynomial based VMC method will significantly reduce the overall problem dimension, which in practice will significantly reduce the computational cost associated with solving nonlinear constrained optimal trajectory problems; (2) the sequential VMC method will improve the solution optimality by sequentially refining certain parameters, such as the prey motion; and (3) the B-spline augmented VMC method will improve the solution iv optimality without sacrificing the CPU time much as compared with the polynomial based approach. Several simulation scenarios, including the Breakwell problem, the phantom track problem, the minimum-time mobile robot obstacle avoidance problem, and the Snell’s river problem are simulated to demonstrate the capabilities of the various forms of the VMC algorithm. The capabilities of the B-spline augmented VMC method are also shown in a hardware demonstration using a mobile robot obstacle avoidance testbed

Advanced control and optimization with applications to complex automotive systems

Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://10.1155/2014/18358

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Deception in Optimal Control

In this paper, we consider an adversarial scenario where one agent seeks to achieve an objective and its adversary seeks to learn the agent's intentions and prevent the agent from achieving its objective. The agent has an incentive to try to deceive the adversary about its intentions, while at the same time working to achieve its objective. The primary contribution of this paper is to introduce a mathematically rigorous framework for the notion of deception within the context of optimal control. The central notion introduced in the paper is that of a belief-induced reward: a reward dependent not only on the agent's state and action, but also adversary's beliefs. Design of an optimal deceptive strategy then becomes a question of optimal control design on the product of the agent's state space and the adversary's belief space. The proposed framework allows for deception to be defined in an arbitrary control system endowed with a reward function, as well as with additional specifications limiting the agent's control policy. In addition to defining deception, we discuss design of optimally deceptive strategies under uncertainties in agent's knowledge about the adversary's learning process. In the latter part of the paper, we focus on a setting where the agent's behavior is governed by a Markov decision process, and show that the design of optimally deceptive strategies under lack of knowledge about the adversary naturally reduces to previously discussed problems in control design on partially observable or uncertain Markov decision processes. Finally, we present two examples of deceptive strategies: a "cops and robbers" scenario and an example where an agent may use camouflage while moving. We show that optimally deceptive strategies in such examples follow the intuitive idea of how to deceive an adversary in the above settings

Reconstruction, Analysis and Synthesis of Collective Motion

As collective motion plays a crucial role in modern day robotics and engineering, it seems appealing to seek inspiration from nature, which abounds with examples of collective motion (starling flocks, fish schools etc.). This approach towards understanding and reverse-engineering a particular aspect of nature forms the foundation of this dissertation, and its main contribution is threefold. First we identify the importance of appropriate algorithms to extract parameters of motion from sampled observations of the trajectory, and then by assuming an appropriate generative model we turn this into a regularized inversion problem with the regularization term imposing smoothness of the reconstructed trajectory. First we assume a linear triple-integrator model, and by penalizing high values of the jerk path integral we reconstruct the trajectory through an analytical approach. Alternatively, the evolution of a trajectory can be governed by natural Frenet frame equations. Inadequacy of integrability theory for nonlinear systems poses the utmost challenge in having an analytic solution, and forces us to adopt a numerical optimization approach. However, by noting the fact that the underlying dynamics defines a left invariant vector field on a Lie group, we develop a framework based on Pontryagin's maximum principle. This approach toward data smoothing yields a semi-analytic solution. Equipped with appropriate algorithms for trajectory reconstruction we analyze flight data for biological motions, and this marks the second contribution of this dissertation. By analyzing the flight data of big brown bats in two different settings (chasing a free-flying praying mantis and competing with a conspecific to catch a tethered mealworm), we provide evidence to show the presence of a context specific switch in flight strategy. Moreover, our approach provides a way to estimate the behavioral latency associated with these foraging behaviors. On the other hand, we have also analyzed the flight data of European starling flocks, and it can be concluded from our analysis that the flock-averaged coherence (the average cosine of the angle between the velocities of a focal bird and its neighborhood center of mass, averaged over the entire flock) gets maximized by considering 5-7 nearest neighbors. The analysis also sheds some light into the underlying feedback mechanism for steering control. The third and final contribution of this dissertation lies in the domain of control law synthesis. Drawing inspiration from coherent movement of starling flocks, we introduce a strategy (Topological Velocity Alignment) for collective motion, wherein each agent aligns its velocity along the direction of motion of its neighborhood center of mass. A feedback law has also been proposed for achieving this strategy, and we have analyzed two special cases (two-body system; and an N-body system with cyclic interaction) to show effectiveness of our proposed feedback law. It has been observed through numerical simulation and robotic implementation that this approach towards collective motion can give rise to a splitting behavior

Visually Adversarial Attacks and Defenses in the Physical World: A Survey

Although Deep Neural Networks (DNNs) have been widely applied in various real-world scenarios, they are vulnerable to adversarial examples. The current adversarial attacks in computer vision can be divided into digital attacks and physical attacks according to their different attack forms. Compared with digital attacks, which generate perturbations in the digital pixels, physical attacks are more practical in the real world. Owing to the serious security problem caused by physically adversarial examples, many works have been proposed to evaluate the physically adversarial robustness of DNNs in the past years. In this paper, we summarize a survey versus the current physically adversarial attacks and physically adversarial defenses in computer vision. To establish a taxonomy, we organize the current physical attacks from attack tasks, attack forms, and attack methods, respectively. Thus, readers can have a systematic knowledge of this topic from different aspects. For the physical defenses, we establish the taxonomy from pre-processing, in-processing, and post-processing for the DNN models to achieve full coverage of the adversarial defenses. Based on the above survey, we finally discuss the challenges of this research field and further outlook on the future direction

Mosquito-inspired Swarming and Pursuit for Autonomous Rotorcraft

The long-term goal of this research is to design cooperative-control algorithms for autonomous vehicles inspired by the collective behaviors in animal groups. The specific research objectives of this dissertation are twofold: (1) to analyze and model the swarming and pursuit behaviors observed in the mating swarms of mosquitoes, and (2) to design mosquito-inspired control algorithms to perform swarming and pursuit with autonomous rotorcraft. The first part of this dissertation analyzes the reconstructed flight data of the malarial mosquito Anopheles gambiae to characterize the velocity-alignment interaction between male mosquitoes, who aggregate to form mating swarms and subsequently pursue a female mosquito. Both swarming and pursuit behaviors are represented using self-propelled particle models. The model is used together with tools from control theory to investigate the connection between velocity-alignment behavior and success in pursuit. The results of this research have a potential impact on vector-control methods for malaria, and are also utilized in the second part of this dissertation. The second part of this dissertation studies two types of pursuit problems inspired by the collective behavior in mosquito swarms. The first problem considers the strategy for a single pursuer chasing a single target. This problem has been studied extensively for the application to missile guidance and navigation. Here, we tailor the assumptions on the dynamics of the agents as well as the design criteria for the application to small and agile rotorcraft. The second pursuit problem incorporates the swarming behavior by considering a scenario in which multiple guardian vehicles are deployed to protect an area against fast intruders. We derive necessary and sufficient conditions for capturing the intruder. We also present swarming strategies to maximize the performance of the guardians, inspired by the random-oscillatory motion and the velocity-alignment behavior of male mosquitoes

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

상승단계 발사체의 최적 궤적 생성 및 강건 제어 기법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 김현진.This research focused on trajectory generation and control of a flexible launch vehicle during ascent flight. An important issue of a launch vehicle design is generating optimal trajectory during its atmospheric ascent flight while satisfying constraints such as aerodynamic load. These constraints become more significant due to wind disturbance, especially in the maximum dynamic pressure region. On the other hand, modern launch vehicles are becoming long and slender for the reduction in structure mass to increase payload. As a result, they possess highly flexible bending modes in addition to aerodynamically unstable rigid body characteristics. This dissertation proposes a rapid and reliable optimization approach for trajectory generation via sequential virtual motion camouflage (VMC) and non-conservative robust control for an unstable and flexible launch vehicle. First, an optimal trajectory is generated in a rapid and reliable manner through the introduction of the virtual motion camouflage. VMC uses an observed biological phenomenon called motion camouflage to construct a subspace in which the solution trajectory is generated. By the virtue of this subspace search, the overall dimension of the optimization problem is reduced, which decreases the computational time significantly compared to a traditional direct input programming. Second, an interactive optimization algorithm is proposed to find a feasible solution easier. For this, the constraint correction step is added after VMC optimization. Since VMC is a subspace problem, a feasible solution may not exist when subspace is not properly constructed. In order to address this concern, a quadratic programming (QP) problem is formulated to find a direction along which the parameters defining the subspace can be improved. Via a computationally fast QP, specific parameters (such as prey and reference point) used in VMC can be refined quickly and sequentially. As a result, the proposed interactive optimization algorithm is less sensitive to the initial guess of the optimization parameters. Third, a non-conservative 2-DOF H infty controller for an unstable and flexible launch vehicle is proposed. The objectives of the control system are to provide sufficient margins for the launch vehicle dynamics and to enhance the speed of the closed-loop response. For this, a robust control approach is used. The key of the control design is to overcome conservativeness of the robust control. The baseline controllers using the optimal control such as LQG and LQI are designed prior to a robust controller. These optimal controllers are used to find a desirable shape of the sensitivity transfer function in order to reduce conservativeness of the robust control. After implementation and analysis of the baseline controllers, an improved sensitivity weighting function is defined as a non-conventional form with different slopes in the low frequency and around crossover frequency, which results in performance enhancement without loss of robustness. A two-degree-of-freedom H infty controller is designed which uses feedback and feedforward control together to improve tracking performance with the proposed sensitivity weighting function as a target closed-loop shape. The resulting H infty controller stabilizes the unstable rigid body dynamics with sufficient margins in the low frequency, and also uses gain stabilization in addition to phase stabilization to handle the lightly damped bending modes in the high-frequency region.1 Introduction 1 1.1 Background and motivations 1 1.2 Literature survey 3 1.2.1 Optimal trajectory generation for a launch vehicle 3 1.2.2 Controller design for a flexible launch vehicle 5 1.3 Research objectives and contributions 6 1.4 Thesis organization 7 2 Launch Vehicle Dynamics 9 2.1 Frame and coordinate 9 2.2 Rigid body motion 9 2.3 Aerodynamic forces and moments 12 2.4 Gravity force 14 2.5 Thrust forces and moments 14 2.6 Flexible bending modes 15 3 Optimal Trajectory Generation 16 3.1 VMC based trajectory optimization 16 3.1.1 Nonlinear constrained trajectory optimization problem 17 3.1.2 VMC formulation 17 3.2 VMC based trajectory optimization applied to the launch vehicle 21 3.2.1 Relationship between launch vehicle dynamics and VMC 21 3.2.2 Selection of reference point and virtual prey motion 23 3.2.3 Trajectory optimization via VMC 25 3.2.4 Sequential VMC: constraint correction 27 3.2.5 Comparison study 29 3.3 Numerical simulations 31 3.3.1 Case 1: No wind disturbance 36 3.3.2 Case 2: Z-axis wind disturbance 39 3.3.3 Case 3: Y -axis wind disturbance 43 3.3.4 Case 4: Z and Y -axes wind disturbance 48 3.3.5 Performance comparison 51 4 Robust Control 57 4.1 Launch vehicle model description 57 4.1.1 Rigid body model 58 4.1.2 Flexible modes and Actuator 59 4.1.3 System properties and design specications 63 4.2 Baseline controllers design 65 4.2.1 Set-point LQG 65 4.2.2 Integral LQG 69 4.3 Robust controller design 74 4.3.1 H infinity control theory 74 4.3.2 Two-degree-of freedom H infinity controller 76 4.3.3 Selection of weighting functions: Wp and Wu 77 4.3.4 Synthesis results 82 4.3.5 Comparison study 88 4.4 Numerical simulation 94 5 Conclusions 98 Abstract (in Korean) 106Docto