The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density.
The decline of natural pollinators necessitates the development of novel pollination technologies. In this work, we propose a drone-enabled autonomous pollination system (APS) that consists of five primary modules: environment sensing, flower perception, path planning, flight control, and pollination mechanisms. These modules are highly dependent upon each other, with each module relying on inputs from the other modules. In this paper, we focus on approaches to the flower perception, path planning, and flight control modules. First, we briefly introduce a flower perception method from our previous work to create a map of flower locations. With a map of flowers, APS path planning is defined as a variant of the Travelling Salesman Problem (TSP). Two path planning approaches are compared based on mixed-integer programming (MIP) and genetic algorithms (GA), respectively. The GA approach is chosen as the superior approach due to the vast computational savings with negligible loss of optimality. To accurately follow the generated path for pollination, we develop a convex optimization approach to the quadrotor flight control problem (QFCP). This approach solves two convex problems. The first problem is a convexified three degree-of-freedom QFCP. The solution to this problem is used as an initial guess to the second convex problem, which is a linearized six degree-of-freedom QFCP. It is found that changing the objective of the second convex problem to minimize the deviation from the initial guess provides improved physical feasibility and solutions similar to a general-purpose optimizer. The path planning and flight control approaches are then tested within a model predictive control (MPC) framework where significant computational savings and embedded adjustments to uncertainty are observed. Coupling the two modules together provides a simple demonstration of how the entire APS will operate in practice.
Recent advances in efficient optimization algorithms and high-performance computing allow the construction of integrated design frameworks wherein the traditionally segregated disciplines such as airframe design, aerodynamics, and trajectory analysis can be coupled together in order to undertake the design and optimization of vehicles as integrated systems in a larger design space. The particular interest of this paper is a potential approach to incorporating high-fidelity aerodynamic models and trajectory optimization techniques in hypersonic vehicle designs by incrementally varying the geometric parameters of the vehicle to observe induced performance variations and optimize these parameters for specific mission profiles using data-driven optimization. First, the exigency of creating integrated design frameworks considering high-fidelity disciplinary models such as aerodynamics and trajectory modeling is justified. Then, an energy-based problem formulation for hypersonic trajectory optimization is introduced. A panel method based on the modified Newtonian flow theory and Eckert’s reference model is used to produce high-fidelity aerodynamic force and heating coefficients, based on which a pseudospectral optimal control package is used to solve for optimal trajectories. Finally, a novel, iterative, data-driven framework employing Bayesian optimization and machine learning is established to integrate these disciplinary models and successively search for the geometry that enables the optimal mission performance. Simulation results demonstrate the feasibility and performance of the developed approach.
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