The paper introduces a quadratic programming algorithm for real-time local path planning of autonomous vehicles. The algorithm relies on discretized sampling points and an enhanced cost function. Initially, we formula...
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The paper introduces a quadratic programming algorithm for real-time local path planning of autonomous vehicles. The algorithm relies on discretized sampling points and an enhanced cost function. Initially, we formulate the cost function to optimize the reference trajectory and establish the Frenet coordinate system. The drivable region undergoes discretization to generate sampling points in the Frenet coordinate system. We apply the principles of convex spatial obstacle avoidance to define the vehicle's drivable area, taking into account the vehicle's kinematics and establishing barrier boundary conditions. Subsequently, quadratic programming is employed to determine an optimal path within the vehicle's drivable area. Concurrently, two cost functions are devised, the first evaluates the distance between the vehicle and obstacles, while the second assesses ride comfort, these cost functions are employed to evaluate sampling points and speed profiles, facilitating the planning of an optimal speed profile on the selected path. Finally, the algorithm undergoes validation through co-simulation using Matlab/Simulink, PreScan, and CarSim software. Various road scenarios, including straight and S-curve roads with both dynamic and static obstacles, are created to assess the method's feasibility. The test results demonstrate the algorithm's efficacy in avoiding moving and stationary obstacles and generating an ideal path compliant with driving conditions.
This note describes a reference governor design for a continuous-time nonlinear system with an additive disturbance. The design is based on predicting the response of the nonlinear system, by the response of a linear ...
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This note describes a reference governor design for a continuous-time nonlinear system with an additive disturbance. The design is based on predicting the response of the nonlinear system, by the response of a linear model with a set-bounded prediction error, where a state-and-input-dependent bound on the prediction error is explicitly characterized using logarithmic norms. The online optimization is reduced to a convex quadratic program with linear inequality constraints. Two numerical examples are reported.
This paper presents an efficient numerical method based on quadratic programming, which may be used to analyze fretting contacts in the presence of wear. The approach provides an alternative to a full finite element a...
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This paper presents an efficient numerical method based on quadratic programming, which may be used to analyze fretting contacts in the presence of wear. The approach provides an alternative to a full finite element analysis, and is much less computationally expensive. Results are presented for wear of a Hertzian contact under full sliding and under partial slip. These are compared with previously published finite element analyses of the same problem. Results are also obtained for the fully worn problem by allowing a large number of wear cycles to accumulate. The predicted traction distributions for this case compare well with the fully worn analytical solution presented in part one of this paper. [DOI: 10.1115/1.4000733]
Two classes of high-performance neural networks for solving linear and quadratic programming problems are given. We prove that the new system converges globally to the solutions of the linear and quadratic programming...
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Two classes of high-performance neural networks for solving linear and quadratic programming problems are given. We prove that the new system converges globally to the solutions of the linear and quadratic programming problems, In a neural network, network parameters are usually not specified. The proposed models can overcome numerical difficulty caused by neural networks with network parameters and obtain desired approximate solutions of the linear and quadratic programming problems.
Many classification techniques have been successfully applied to credit scoring tasks. However, using them blindly may lead to unsatisfactory results. Generally, credit datasets are large and are characterized by redu...
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Many classification techniques have been successfully applied to credit scoring tasks. However, using them blindly may lead to unsatisfactory results. Generally, credit datasets are large and are characterized by redundant features and nonrelevant data. Hence, classification techniques and model accuracy could be hampered. To overcome this problem, this study explores a variety of filter and wrapper feature selection methods for reducing nonrelevant features. We argue that these two types of selection techniques are complementary to each other. A fusion strategy is then proposed to sequentially combine the ranking criteria of multiple filters and a wrapper method. Evaluations on three credit datasets show that feature subsets selected by fusion methods are either superior to or at least as adequate as those selected by individual methods.
The authors propose a super-resolution time difference of arrival (TDOA) estimator. In the proposed approach TDOA estimation is first converted into a parameter estimation problem of sinusoidal signals with low-pass e...
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The authors propose a super-resolution time difference of arrival (TDOA) estimator. In the proposed approach TDOA estimation is first converted into a parameter estimation problem of sinusoidal signals with low-pass envelope. Then TDOAs are estimated based on eigenanalysis and sequential quadratic programming (ESQP). Compared with the conventional approaches, the proposed method is applicable to signals with narrowband spectra. Furthermore, it does not require a priori knowledge of the transmitted signal. Simulation results demonstrate the improved performance of the proposed method when compared to the conventional correlation and MUSIC algorithm.
Some new properties of the Projection DC decomposition algorithm (we call it Algorithm A) and the Proximal DC decomposition algorithm (we call it Algorithm B) Pham Dinh et al. in Optim Methods Softw, 23(4): 609-629 (2...
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Some new properties of the Projection DC decomposition algorithm (we call it Algorithm A) and the Proximal DC decomposition algorithm (we call it Algorithm B) Pham Dinh et al. in Optim Methods Softw, 23(4): 609-629 (2008) for solving the indefinite quadratic programming problem under linear constraints are proved in this paper. Among other things, we show that DCA sequences generated by Algorithm A converge to a locally unique solution if the initial points are taken from a neighborhood of it, and DCA sequences generated by either Algorithm A or Algorithm B are all bounded if a condition guaranteeing the solution existence of the given problem is satisfied.
This paper describes a method for obtaining a mobile manipulator's motion sequence by indicating a goal-hand pose. The proposed method entails recording various robot motions as a large number of motion sequences ...
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This paper describes a method for obtaining a mobile manipulator's motion sequence by indicating a goal-hand pose. The proposed method entails recording various robot motions as a large number of motion sequences and associated swept volumes (SVs) and then selecting the most appropriate SV for the current situation. In addition, the motion sequences can be altered while the robot is in motion, and the transition motion is generated using sequential quadratic programming, enabling the robot to avoid collisions with obstacles found after it has begun to move. The proposed method's performance is verified by simulation to confirm the appropriate amount of data and the method's superiority.
In this note we specify a necessary and sufficient condition for global optimality in concave quadratic minimization problems. Using this condition, it follows that, from the perspective of worst-case complexity of co...
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In this note we specify a necessary and sufficient condition for global optimality in concave quadratic minimization problems. Using this condition, it follows that, from the perspective of worst-case complexity of concave quadratic problems, the difference between local and global optimality conditions is not as large as in general. As an essential ingredient, we here use the epsilon-subdifferential calculus via an approach of Hiriart-Urruty and Lemarechal (1990).
Equality-constrained quadratic programming (QP) has been one of the most basic and typical problems in the Internet of Things domain. In big data scenarios, how to quickly and accurately solve the problem in hardware ...
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Equality-constrained quadratic programming (QP) has been one of the most basic and typical problems in the Internet of Things domain. In big data scenarios, how to quickly and accurately solve the problem in hardware has not been realized. Therefore, in this article, a memristive recurrent neural circuit that can parallel solve the QP problem in real time is proposed. First, a new memristive synaptic array is designed that can simultaneously implement parallel reading and writing. On the basis of this structure, a new neural network circuit based on memristor is designed that can perform large-scale recursive operations by parallel methods. This circuit can solve the equality-constrained QP problem in different situations by using such real-time programmable memristor arrays processing in memory. The PSpice simulation results show that the problem can be solved with 99.8% precision. Based on practical verification, the neural circuit experiment on PCB is presented with 97.34% precision. Moreover, the circuit has good robustness under the interference of weight value. And, it has an advantage in processing time compared with FPGA.
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