6,048 research outputs found
Robust Mission Design Through Evidence Theory and Multi-Agent Collaborative Search
In this paper, the preliminary design of a space mission is approached
introducing uncertainties on the design parameters and formulating the
resulting reliable design problem as a multiobjective optimization problem.
Uncertainties are modelled through evidence theory and the belief, or
credibility, in the successful achievement of mission goals is maximised along
with the reliability of constraint satisfaction. The multiobjective
optimisation problem is solved through a novel algorithm based on the
collaboration of a population of agents in search for the set of highly
reliable solutions. Two typical problems in mission analysis are used to
illustrate the proposed methodology
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014
Inside the brain of an elite athlete: The neural processes that support high achievement in sports
Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance
Learning High-Level Policies for Model Predictive Control
The combination of policy search and deep neural networks holds the promise
of automating a variety of decision-making tasks. Model Predictive
Control~(MPC) provides robust solutions to robot control tasks by making use of
a dynamical model of the system and solving an optimization problem online over
a short planning horizon. In this work, we leverage probabilistic
decision-making approaches and the generalization capability of artificial
neural networks to the powerful online optimization by learning a deep
high-level policy for the MPC~(High-MPC). Conditioning on robot's local
observations, the trained neural network policy is capable of adaptively
selecting high-level decision variables for the low-level MPC controller, which
then generates optimal control commands for the robot. First, we formulate the
search of high-level decision variables for MPC as a policy search problem,
specifically, a probabilistic inference problem. The problem can be solved in a
closed-form solution. Second, we propose a self-supervised learning algorithm
for learning a neural network high-level policy, which is useful for online
hyperparameter adaptations in highly dynamic environments. We demonstrate the
importance of incorporating the online adaption into autonomous robots by using
the proposed method to solve a challenging control problem, where the task is
to control a simulated quadrotor to fly through a swinging gate. We show that
our approach can handle situations that are difficult for standard MPC
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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