Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

Sampling-based Motion Planning via Control Barrier Functions

Robot motion planning is central to real-world autonomous applications, such as self-driving cars, persistence surveillance, and robotic arm manipulation. One challenge in motion planning is generating control signals for nonlinear systems that result in obstacle free paths through dynamic environments. In this paper, we propose Control Barrier Function guided Rapidly-exploring Random Trees (CBF-RRT), a sampling-based motion planning algorithm for continuous-time nonlinear systems in dynamic environments. The algorithm focuses on two objectives: efficiently generating feasible controls that steer the system toward a goal region, and handling environments with dynamical obstacles in continuous time. We formulate the control synthesis problem as a Quadratic Program (QP) that enforces Control Barrier Function (CBF) constraints to achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest neighbor or collision checks when sampling, which greatly reduce the run-time overhead when compared to standard RRT variants

Processing of aluminum-graphite particulate metal matrix composites by advanced shear technology

Copyright @ 2009 ASM International. This paper was published in Journal of Materials Engineering and Performance 18(9) and is made available as an electronic reprint with the permission of ASM International. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplications of any material in this paper for a fee or for commercial purposes, or modification of the content of this paper are prohibited.To extend the possibilities of using aluminum/graphite composites as structural materials, a novel process is developed. The conventional methods often produce agglomerated structures exhibiting lower strength and ductility. To overcome the cohesive force of the agglomerates, a melt conditioned high-pressure die casting (MC-HPDC) process innovatively adapts the well-established, high-shear dispersive mixing action of a twin screw mechanism. The distribution of particles and properties of composites are quantitatively evaluated. The adopted rheo process significantly improved the distribution of the reinforcement in the matrix with a strong interfacial bond between the two. A good combination of improved ultimate tensile strength (UTS) and tensile elongation (e) is obtained compared with composites produced by conventional processes.EPSR

Multi-objective UAS flight management in time constrained low altitude local environments

This paper presents a new framework for Multi-Objective Flight Management of Unmanned Aerial Systems (UAS), operating in partially known environments, where planning time constraints are present. During UAS operations, civilian UAS may have multiple objectives to meet including: platform safety; minimizing fuel, time, distance; and minimizing deviation from the current path. The planning layers within the framework use multi-objective optimization to converge to a solution which better reflects overall mission requirements. The solution must be generated within the available decision window, else the UAS must enter a safety state; this potentially limits mission efficiency. Local or short range planning at low altitudes requires the classification of terrain and infrastructure in proximity as potential obstacles. The potential increase in the number of obstacles present further reduces the decision window in comparison to high altitude flight. A novel Flight Management System (FMS) has been incorporated within the framework to moderate the time available to the environment abstraction, path and trajectory planning layers for more efficient use of the available decision window. Enabling the FMS during simulation increased the optimality of the output trajectory on systems with sufficient computational power to run the algorithm in real time. Conversely, the FMS found sub-optimal solutions for the system with insufficient computational capability once the objective utility threshold was decreased from 0.95 to 0.85. This allowed the UAS to continue operations without having to resort to entering a safe state