1,885 research outputs found
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
Evaluation of model predictive control method for collision avoidance of automated vehicles
Indiana University-Purdue University Indianapolis (IUPUI)Collision avoidance design plays an essential role in autonomous vehicle technology.
It's an attractive research area that will need much experimentation in the
future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under diFFerent circumstances
for safety before use in real life.
This thesis proposes a method for designing and presenting a collision avoidance
maneuver by using a model predictive controller with a moving obstacle for automated
vehicles. It consists of a plant model, an adaptive MPC controller, and a reference
trajectory. The proposed strategy applies a dynamic bicycle model as the plant
model, adaptive model predictive controller for the lateral control, and a custom
reference trajectory for the scenario design. The model was developed using the
Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin
tools available in Matlab/Simulink were used to verify the modeling approach and
analyze the performance of the system.
The major contribution of this thesis work was implementing a novel dynamic
obstacle avoidance control method for automated vehicles. The study used validated
parameters obtained from previous research. The novelty of this research was performing
the studies using a MPC based controller instead of a sliding mode controller,
that was primarily used in other studies. The results obtained from the study are compared
with the validated models. The comparisons consisted of the lateral overlap,
lateral error, and steering angle simulation results between the models. Additionally,
this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced
reasonably acceptable results and recommendations for future studies
Design of an Active-Assistance Balancing Mechanism for a Bicycle
The goal of this project is to design and build a prototype self balancing bicycle for use as a teaching tool for someone learning to ride a bicycle and as means for a disabled person to ride a bicycle who would otherwise not be able to do so. The project consists of a research phase in which similar systems have been investigated to help determine a sensible design approach and to establish appropriate design specifications; a design phase in which a prototype was designed to meet the aforementioned specifications; and a construction phase, in which the prototype was built and tested
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