20,442 research outputs found
Contact-Implicit Trajectory Optimization using Orthogonal Collocation
In this paper we propose a method to improve the accuracy of trajectory
optimization for dynamic robots with intermittent contact by using orthogonal
collocation. Until recently, most trajectory optimization methods for systems
with contacts employ mode-scheduling, which requires an a priori knowledge of
the contact order and thus cannot produce complex or non-intuitive behaviors.
Contact-implicit trajectory optimization methods offer a solution to this by
allowing the optimization to make or break contacts as needed, but thus far
have suffered from poor accuracy. Here, we combine methods from direct
collocation using higher order orthogonal polynomials with contact-implicit
optimization to generate trajectories with significantly improved accuracy. The
key insight is to increase the order of the polynomial representation while
maintaining the assumption that impact occurs over the duration of one finite
element
Optimization problems with low SWaP tactical Computing
In a resource-constrained, contested environment, computing resources need to
be aware of possible size, weight, and power (SWaP) restrictions. SWaP-aware
computational efficiency depends upon optimization of computational resources
and intelligent time versus efficiency tradeoffs in decision making. In this
paper we address the complexity of various optimization strategies related to
low SWaP computing. Due to these restrictions, only a small subset of less
complicated and fast computable algorithms can be used for tactical, adaptive
computing.Comment: 8 pages, 1 figure. To appear in Proc. SPI
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
Self-driving vehicles are a maturing technology with the potential to reshape
mobility by enhancing the safety, accessibility, efficiency, and convenience of
automotive transportation. Safety-critical tasks that must be executed by a
self-driving vehicle include planning of motions through a dynamic environment
shared with other vehicles and pedestrians, and their robust executions via
feedback control. The objective of this paper is to survey the current state of
the art on planning and control algorithms with particular regard to the urban
setting. A selection of proposed techniques is reviewed along with a discussion
of their effectiveness. The surveyed approaches differ in the vehicle mobility
model used, in assumptions on the structure of the environment, and in
computational requirements. The side-by-side comparison presented in this
survey helps to gain insight into the strengths and limitations of the reviewed
approaches and assists with system level design choices
Stabilization of polynomial dynamical systems using linear programming based on Bernstein polynomials
In this paper, we deal with the problem of synthesizing static output
feedback controllers for stabilizing polynomial systems. Our approach jointly
synthesizes a Lyapunov function and a static output feedback controller that
stabilizes the system over a given subset of the state-space. Specifically, our
approach is simultaneously targeted towards two goals: (a) asymptotic Lyapunov
stability of the system, and (b) invariance of a box containing the
equilibrium. Our approach uses Bernstein polynomials to build a linear
relaxation of polynomial optimization problems, and the use of a so-called
"policy iteration" approach to deal with bilinear optimization problems. Our
approach can be naturally extended to synthesizing hybrid feedback control laws
through a combination of state-space decomposition and Bernstein polynomials.
We demonstrate the effectiveness of our approach on a series of numerical
benchmark examples
Latin Puzzles
Based on a previous generalization by the author of Latin squares to Latin
boards, this paper generalizes partial Latin squares and related objects like
partial Latin squares, completable partial Latin squares and Latin square
puzzles. The latter challenge players to complete partial Latin squares, Sudoku
being the most popular variant nowadays.
The present generalization results in partial Latin boards, completable
partial Latin boards and Latin puzzles. Provided examples of Latin puzzles
illustrate how they differ from puzzles based on Latin squares. The examples
include Sudoku Ripeto and Custom Sudoku, two new Sudoku variants. This is
followed by a discussion of methods to find Latin boards and Latin puzzles
amenable to being solved by human players, with an emphasis on those based on
constraint programming. The paper also includes an analysis of objective and
subjective ways to measure the difficulty of Latin puzzles.Comment: 41 pages, 31 figure
Formal Synthesis of Stochastic Systems via Control Barrier Certificates
This paper focuses on synthesizing control policies for discrete-time
stochastic control systems together with a lower bound on the probability that
the systems satisfy the complex temporal properties. The desired properties of
the system are expressed as linear temporal logic (LTL) specifications over
finite traces. In particular, our approach decomposes the given specification
into simpler reachability tasks based on its automata representation. We then
propose the use of so-called \emph{control barrier certificate} to solve those
simpler reachability tasks along with computing the corresponding controllers
and probability bounds. Finally, we combine those controllers to obtain a
hybrid control policy solving the considered problem. Under some assumptions,
we also provide two systematic approaches for uncountable and finite input sets
to search for control barrier certificates. We demonstrate the effectiveness of
the proposed approach on a room temperature control and lane-keeping of a
vehicle modeled as a four-dimensional single-track kinematic model. We compare
our results with the discretization-based methods in the literature.Comment: 22 pages, 11 figures. arXiv admin note: text overlap with
arXiv:1807.0006
Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine
Support Vector Machine (SVM) is an effective model for many classification
problems. However, SVM needs the solution of a quadratic program which require
specialized code. In addition, SVM has many parameters, which affects the
performance of SVM classifier. Recently, the Generalized Eigenvalue Proximal
SVM (GEPSVM) has been presented to solve the SVM complexity. In real world
applications data may affected by error or noise, working with this data is a
challenging problem. In this paper, an approach has been proposed to overcome
this problem. This method is called DSA-GEPSVM. The main improvements are
carried out based on the following: 1) a novel fuzzy values in the linear case.
2) A new Kernel function in the nonlinear case. 3) Differential Search
Algorithm (DSA) is reformulated to find near optimal values of the GEPSVM
parameters and its kernel parameters. The experimental results show that the
proposed approach is able to find the suitable parameter values, and has higher
classification accuracy compared with some other algorithms
Programmable Cellular Automata Based Efficient Parallel AES Encryption Algorithm
Cellular Automata(CA) is a discrete computing model which provides simple,
flexible and efficient platform for simulating complicated systems and
performing complex computation based on the neighborhoods information. CA
consists of two components 1) a set of cells and 2) a set of rules .
Programmable Cellular Automata(PCA) employs some control signals on a Cellular
Automata(CA) structure. Programmable Cellular Automata were successfully
applied for simulation of biological systems, physical systems and recently to
design parallel and distributed algorithms for solving task density and
synchronization problems. In this paper PCA is applied to develop cryptography
algorithms. This paper deals with the cryptography for a parallel AES
encryption algorithm based on programmable cellular automata. This proposed
algorithm based on symmetric key systems
Fuel Minimisation for a Vehicle Equipped with a Flywheel and Battery on a Three-Dimensional Track
An optimal control based methodology is proposed for minimising the
combustible fuel consumption of a hybrid vehicle equipped with an internal
combustion engine, a high-speed flywheel and a battery. The
three-dimensionality of the road is recognised by the optimal control
calculations. Fuel efficiency is achieved by optimally exploiting the primary
and secondary energy sources and controlling the vehicle so that the fuel
consumption is minimised for a given, but arbitrary three-dimensional route. A
time-of-arrival constraint rather than a driving cycle is used. The benefits of
using multiple auxiliary storage systems are demonstrated and a lower-bound
estimate of the fuel consumption is presented
Controller Synthesis for Discrete-time Hybrid Polynomial Systems via Occupation Measures
We consider the feedback design for stabilizing a rigid body system by making
and breaking multiple contacts with the environment without prespecifying the
timing or the number of occurrence of the contacts. We model such a system as a
discrete-time hybrid polynomial system, where the state-input space is
partitioned into several polytopic regions with each region associated with a
different polynomial dynamics equation. Based on the notion of occupation
measures, we present a novel controller synthesis approach that solves
finite-dimensional semidefinite programs as approximations to an
infinite-dimensional linear program to stabilize the system. The optimization
formulation is simple and convex, and for any fixed degree of approximations
the computational complexity is polynomial in the state and control input
dimensions. We illustrate our approach on some robotics examples.Comment: Accepted by ICRA 2019. Some text overlap with arXiv:1803.09022 in
introducing standard notations and preliminary knowledge. arXiv admin note:
text overlap with arXiv:1803.0902
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