Development of Autonomous Robot with Simple Navigation System for Tsukuba Challenge 2015

2016 ◽  
Vol 28 (4) ◽  
pp. 432-440 ◽  
Author(s):  
Yuta Kanuki ◽  
◽  
Naoya Ohta
Keyword(s):  
Computational Cost ◽  
Map Matching ◽  
Shape Information ◽  
Environment Map ◽  
Rainy Weather ◽  
Laser Range Finders ◽  
Fixed Type ◽  
Sensor Configuration ◽  
Binary Image Processing ◽  
And Control

[abstFig src='/00280004/01.jpg' width='300' text='MercuryMega (SICKLaser-Model)' ] This paper introduces the robot that was developed for Tsukuba Challenge 2015. One of the team’s design goals was to implement a simple sensor configuration and control algorithm. For example, the robot’s laser range finders (LRF) were of fixed type, and localization was accomplished using only a single LRF and no other sensor. Environment map matching was achieved using an algorithm that processed pyramid images using binary image processing to reduce computational cost. Shape information from the LRF and color information from a camera were combined to detect a signboard placed near a person. During an actual test in rainy weather, the robot ran the entire course and, detected two, out of a possible four, number of target persons.

Model reduction of aerothermodynamic for hypersonic aerothermoelasticity based on POD and Chebyshev method

2018 ◽  
Vol 233 (10) ◽  
pp. 3734-3748
Author(s):  
Yan Xiaoxuan ◽  
Han Jinglong ◽  
Zhang Bing ◽  
Yuan Haiwei
Keyword(s):  
Computational Cost ◽  
Maximum Error ◽  
Control Surface ◽  
Alternative Approach ◽  
Average Maximum ◽  
Optimization And Control ◽  
Efficient Alternative ◽  
Computational Fluid Dynamics Cfd ◽  
And Control ◽  
Proper Orthogonal

Accurate modeling of aerothermodynamics with low computational cost takes on a crucial role for the optimization and control of hypersonic vehicles. This study examines three reduced-order models (ROMs) to provide a reliable and efficient alternative approach for obtaining the aerothermodynamics of a hypersonic control surface. Coupled computational fluid dynamics (CFD) and computational thermostructural dynamics (CTSD) approaches are used to generate the snapshots for ROMs considering the interactions between aerothermodynamics, structural dynamics and heat transfer. One ROM adopts a surrogate approach named Kriging. The second ROM is constructed by the combination of Proper Orthogonal Decomposition (POD) and Kriging, namely, POD-Kriging. The accuracy of Kriging-based ROM is higher than that of POD-Kriging-based ROM, but the efficiency is lower. Therefore, to address the shortcomings of the above two approaches, a new ROM is developed that is composed of POD and modified Chebyshev polynomials, namely, POD-Chebyshev. The ROM based on POD-Chebyshev has the best precision and efficiency among the three ROMs and generally has less than 2% average maximum error for the studied problem.

Control and Design Optimization of a Novel Hybrid Electric Powertrain System

2017 ◽  
Author(s):  
Chenyu Yi ◽  
Bogdan Epureanu
Keyword(s):  
Optimal Control ◽  
Design Optimization ◽  
Fuel Economy ◽  
Optimization Problems ◽  
Computational Cost ◽  
Electric Machines ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
And Control ◽  
Electric Powertrains

Control and design optimization of hybrid electric powertrains is necessary to maximize the benefits of novel architectures. Previous studies have proposed multiple optimal and near-optimal control methods, approaches for design optimization, and ways to solve coupled design and control optimization problems for hybrid electric powertrains. This study presents control and design optimization of a novel hybrid electric powertrain architecture to evaluate its performance and potential using physics-based models for the electric machines, the battery and a near-optimal control, namely the equivalent consumption minimization strategy. Design optimization in this paper refers to optimizing the sizes of the powertrain components, i.e. electric machines, battery and final drive. The control and design optimization problem is formulated using nested approach with sequential quadratic programming as design optimization method. Metamodeling is applied to abstract the near-optimal powertrain control model to reduce the computational cost. Fuel economy, sizes of components, and consistency of city and highway fuel economy are reported to evaluate the performance of the powertrain designs. The results suggest an optimal powertrain design and control that grants good performance. The optimal design is shown to be robust and non-sensitive to slight component size changes when evaluated for the near-optimal control.

scholarly journals A description of the FAMOUS (version XDBUA) climate model and control run

2008 ◽  
Vol 1 (1) ◽  
pp. 53-68 ◽  
Author(s):  
R. S. Smith ◽  
J. M. Gregory ◽  
A. Osprey
Keyword(s):  
General Circulation ◽  
High Performance ◽  
Climate Model ◽  
Computational Cost ◽  
Circulation Model ◽  
Cold Bias ◽  
Northern Latitudes ◽  
Climate Simulations ◽  
Atmosphere General Circulation Model ◽  
And Control

Abstract. FAMOUS is an ocean-atmosphere general circulation model of low resolution, capable of simulating approximately 120 years of model climate per wallclock day using current high performance computing facilities. It uses most of the same code as HadCM3, a widely used climate model of higher resolution and computational cost, and has been tuned to reproduce the same climate reasonably well. FAMOUS is useful for climate simulations where the computational cost makes the application of HadCM3 unfeasible, either because of the length of simulation or the size of the ensemble desired. We document a number of scientific and technical improvements to the original version of FAMOUS. These improvements include changes to the parameterisations of ozone and sea-ice which alleviate a significant cold bias from high northern latitudes and the upper troposphere, and the elimination of volume-averaged drifts in ocean tracers. A simple model of the marine carbon cycle has also been included. A particular goal of FAMOUS is to conduct millennial-scale paleoclimate simulations of Quaternary ice ages; to this end, a number of useful changes to the model infrastructure have been made.

A Quantitative Quality Measurement for Codebook in Feature Encoding Strategies

2017 ◽  
Vol 21 (7) ◽  
pp. 1232-1239
Author(s):  
Yuki Shinomiya ◽  
◽  
Yukinobu Hoshino
Keyword(s):  
Feature Vector ◽  
Recognition Performance ◽  
Computational Cost ◽  
Quality Measurement ◽  
Practical Applications ◽  
Prior Probabilities ◽  
Feature Encoding ◽  
Encoding Strategies ◽  
Encoding Strategy ◽  
And Control

Nowadays, a feature encoding strategy is a general approach to represent a document, an image or audio as a feature vector. In image recognition problems, this approach treats an image as a set of partial feature descriptors. The set is then converted to a feature vector based on basis vectors called codebook. This paper focuses on a prior probability, which is one of codebook parameters and analyzes dependency for the feature encoding. In this paper, we conducted the following two experiments, analysis of prior probabilities in state-of-the-art encodings and control of prior probabilities. The first experiment investigates the distribution of prior probabilities and compares recognition performances of recent techniques. The results suggest that recognition performance probably depends on the distribution of prior probabilities. The second experiment tries further statistical analysis by controlling the distribution of prior probabilities. The results show a strong negative linear relationship between a standard deviation of prior probabilities and recognition accuracy. From these experiments, the quality of codebook used for feature encoding can be quantitatively measured, and recognition performances can be improved by optimizing codebook. Besides, the codebook is created at an offline step. Therefore, optimizing codebook does not require any additional computational cost for practical applications.

Three Tiered Self-Localization of Two Position Estimation Using Three Dimensional Environment Map and Gyro-Odometry

2014 ◽  
Vol 26 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Kazuya Okawa ◽  
Keyword(s):  
Three Dimensional ◽  
Position Estimation ◽  
Map Matching ◽  
Localization Method ◽  
Challenge Course ◽  
Environment Map ◽  
Improve Accuracy ◽  
Environment Maps ◽  
Using Data ◽  
Map Data

As in the Tsukuba Challenge, any robot that autonomously moves around outdoors must be capable of accurate self-localization. Among many existing methods for robot self-localization, the most widely used is for the robot to estimate its position by comparing it with prior map data actually acquired using its sensor while it moves around. Although we use such a self-localization method in this study, this paper proposes a new method to improve accuracy in robot self-localization. In environments with few detected objects, a lack of acquired data very likely will lead to a failure in map matching and to erroneous robot self-localization. Therefore, a method for robot self-localization that uses three-dimensional environment maps and gyro-odometry depending on the situation is proposed. Moreover, the effectiveness of the proposed method is confirmed by using data from the 2013 Tsukuba Challenge course.

Real-Time Optimal Coherent Phantom Track Generation via the Virtual Motion Camouflage Approach

2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Yunjun Xu ◽  
Gareth Basset
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Inequality Constraints ◽  
Early Termination ◽  
Planning Problem ◽  
Motion Camouflage ◽  
Equality And Inequality Constraints ◽  
Time Optimal ◽  
And Control ◽  
Air Vehicles

Coherent phantom track generation through controlling a group of electronic combat air vehicles is currently an area of great interest to the defense agency for the purpose of deceiving a radar network. However, generating an optimal or even feasible coherent phantom trajectory in real-time is challenging due to the high dimensionality of the problem and severe geometric, as well as state, control, and control rate constraints. In this paper, the bio-inspired virtual motion camouflage based methodology, augmented with the derived early termination condition, is investigated to solve this constrained collaborative trajectory planning problem in two approaches: centralized (one optimization loop) and decentralized (two optimization loops). Specifically, in the decentralized approach, the first loop finds feasible phantom tracks based on the early termination condition and the equality and inequality constraints of the phantom track. The second loop uses the virtual motion camouflage method to solve for the optimal electronic combat air vehicle trajectories based on the feasible phantom tracks obtained in the first loop. Necessary conditions are proposed for both approaches so that the initial and final velocities of the phantom and electronic combat air vehicles are coherent. It is shown that the decentralized approach can solve the problem much faster than the centralized one, and when the decentralized approach is applied, the computational cost remains roughly the same for the cases when the number of nodes and/or the number of electronic combat air vehicles increases. It is concluded that the virtual motion camouflage based decentralized approach has promising potential for usage in real-time implementation.

scholarly journals Model predictive control with random batch methods for a guiding problem

2021 ◽  
pp. 1-24
Author(s):  
Dongnam Ko ◽  
Enrique Zuazua
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Computational Cost ◽  
Original System ◽  
Time Interval ◽  
Short Time Interval ◽  
Feedback Control Strategy ◽  
Short Time ◽  
And Control

We model, simulate and control the guiding problem for a herd of evaders under the action of repulsive drivers. The problem is formulated in an optimal control framework, where the drivers (controls) aim to guide the evaders (states) to a desired region of the Euclidean space. The numerical simulation of such models quickly becomes unfeasible for a large number of interacting agents, as the number of interactions grows [Formula: see text] for [Formula: see text] agents. For reducing the computational cost to [Formula: see text], we use the Random Batch Method (RBM), which provides a computationally feasible approximation of the dynamics. First, the considered time interval is divided into a number of subintervals. In each subinterval, the RBM randomly divides the set of particles into small subsets (batches), considering only the interactions inside each batch. Due to the averaging effect, the RBM approximation converges to the exact dynamics in the [Formula: see text]-expectation norm as the length of subintervals goes to zero. For this approximated dynamics, the corresponding optimal control can be computed efficiently using a classical gradient descent. The resulting control is not optimal for the original system, but for a reduced RBM model. We therefore adopt a Model Predictive Control (MPC) strategy to handle the error in the dynamics. This leads to a semi-feedback control strategy, where the control is applied only for a short time interval to the original system, and then compute the optimal control for the next time interval with the state of the (controlled) original dynamics. Through numerical experiments we show that the combination of RBM and MPC leads to a significant reduction of the computational cost, preserving the capacity of controlling the overall dynamics.

The control of the electro-hydraulic shaking table based on dynamic surface adaptive robust control

2016 ◽  
Vol 39 (8) ◽  
pp. 1271-1280 ◽  
Author(s):  
Wei Shen ◽  
Jun-zheng Wang ◽  
Shou-kun Wang
Keyword(s):  
Robust Control ◽  
Computational Cost ◽  
Adaptive Robust Control ◽  
Working Environment ◽  
Shaking Table ◽  
Parameter Uncertainties ◽  
Robust Controller ◽  
Dynamic Surface ◽  
Vibration Direction ◽  
And Control

The electro-hydraulic shaking table is investigated, in the present paper, to simulate the vibrational working environment of industrial components and equipment. Adaptive robust control can be applied to the shaking table system because electro-hydraulic systems suffer from internal parameter uncertainties and external disturbances. However, the adaptive robust controller design is complicated and has a large computational cost owing to the ‘explosion of terms’ problem. Thus dynamic surface control is applied in the design procedure of adaptive robust controllers to overcome the ‘explosion of terms’ problem. In this work, dynamic surface adaptive robust control is proposed. It simplifies the designed procedure of the controller and decreases its computational cost. Firstly, the structure of a shaking table is formulated and the operation principles of the shaking table, including the hydraulic and control principles, are analysed. A change is made in the mechanical-hydraulic system of the fluid circuit to address the problem of changing the vibration direction. Secondly, a dynamic model of a shaking table is proposed. Based on analysis of this model, the design of a dynamic surface adaptive robust controller for a shaking table is presented so as to improve its performance. Finally, comparative simulations and experiments are carried out. The comparison of performance results with proportional-integral-derivative control verify the correctness of the hydraulic scheme and control principle, as well as the high-performance of the dynamic surface adaptive robust controller. The shaking table achieves a guaranteed dynamical performance and tracking accuracy for the output in the presence of parameter and load uncertainties.

Combined Robust Design and Robust Control of an Electric DC Motor

2006 ◽  
Author(s):  
Sulaiman F. Alyaqout ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
Robust Control ◽  
Robust Design ◽  
Computational Cost ◽  
Minimax Problem ◽  
Dc Motor ◽  
Engineering Systems ◽  
Control Optimization ◽  
Minimax Design ◽  
And Control

System performance can significantly benefit from optimally integrating the design and control of engineering systems. To improve the robustness properties of systems, the present article introduces an approach that combines robust design with robust control and investigates the coupling between them. However, the computational cost of improving this robustness can often be high due to the need to solve a resulting minimax design and control optimization problem. To reduce this cost, sequential and iterative strategies are proposed and compared to an all-in-one strategy for solving the minimax problem. These strategies are then illustrated for a case-study: Robust design and robust control of a DC motor. Results show that the resulting strategies can improve the robustness properties of the DC motor. In addition, the coupling strength between robust design and robust control tends to increase as the applied level of uncertainty increases.

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