scholarly journals Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4448
Author(s):  
Young-Dae Hong ◽  
Young-Joo Kim ◽  
Ki-Baek Lee
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Online Optimization ◽  
Experimental Results ◽  
Sensor Data ◽  
Optimal Position ◽  
Optimization Criteria ◽  
Time Calculation ◽  
The Cost ◽  
Packing System

This paper proposes a novel online object-packing system which can measure the dimensions of every incoming object and calculate its desired position in a given container. Existing object-packing systems have the limitations of requiring the exact information of objects in advance or assuming them as boxes. Thus, this paper is mainly focused on the following two points: (1) Real-time calculation of the dimensions and orientation of an object; (2) Online optimization of the object’s position in a container. The dimensions and orientation of the object are obtained using an RGB-D sensor when the object is picked by a manipulator and moved over a certain position. The optimal position of the object is calculated by recognizing the container’s available space using another RGB-D sensor and minimizing the cost function that is formulated by the available space information and the optimization criteria inspired by the way people place things. The experimental results show that the proposed system successfully places the incoming various shaped objects in their proper positions.

scholarly journals Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

2020 ◽  
Vol 34 (06) ◽  
pp. 9827-9834
Author(s):  
Maximilian Fickert ◽  
Tianyi Gu ◽  
Leonhard Staut ◽  
Wheeler Ruml ◽  
Joerg Hoffmann ◽  
...  
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Experimental Results ◽  
Data Driven ◽  
Heuristic Search Algorithms ◽  
Original Presentation ◽  
Performance Statistics ◽  
The Cost

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.

Study on Power Delivering in Energy Management for Hybrid Electric Vehicle

2013 ◽  
Vol 676 ◽  
pp. 235-241
Author(s):  
Ping Sun ◽  
Xiu Min Yu ◽  
Wei Dong
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Electric Vehicle ◽  
Energy Management ◽  
Minimization Problem ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Global Minimization ◽  
Optimal Value ◽  
The Cost

The equivalent consumption minimization strategy (ECMS) is a method to reduce the global minimization problem to an instantaneous minimization problem to be solved at each instant. The adaptive ECMS is a development of ECMS in which the equivalence factors are not pre-coded, but rather calculated online. The equivalence factors, their optimal value, which minimizes the cost function while maintaining the vehicle substantially charge sustaining, depends on the specific driving cycle. The method proposed in this paper is one of the most important simplifications for actual real time implementation of A-ECMS and power delivering in energy management for HEV. The charging factor can be calculated if the discharging factor is calculated in the experiment for real time. And only a subset of (charging and discharging factors) generates a trend close to zero which indicates charge-sustainability.

World representation and path planning for a mobile robot

Robotica ◽  
1988 ◽  
Vol 6 (1) ◽  
pp. 35-40 ◽  
Author(s):  
E. Palma-Villalon ◽  
P. Dauchez
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Experimental Results ◽  
Spatial Relationships ◽  
Simple Representation ◽  
Matrix Description ◽  
Path Planner ◽  
The Cost

SUMMARYThis paper is related to the problem of navigation of a mobile robot amidst obstacles. In order to easily take into account any modification of the environment, we propose a very simple representation of the obstacles, based on the use of rectangles, as well as a matrix description of the spatial relationships between the obstacles. We also present a path planner based on a A* algorithm, the features of which are specifically designed for our world of rectangles. The cost function takes into account both the length of the path and the number of turns. Some experimental results and implementation details are also given in this paper.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2018 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating an cost function, it is suitably analysed and then a dynamic penalty function to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for the real-time energy management.

scholarly journals A PRELIMINARY WORK ON LAYOUT SLAM FOR RECONSTRUCTION OF INDOOR CORRIDOR ENVIRONMENTS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 295-302
Author(s):  
A. Baligh Jahromi ◽  
G. Sohn ◽  
M. Shahbazi ◽  
J. Kang
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Feature Matching ◽  
Estimation Method ◽  
Single Image ◽  
Video Frames ◽  
Localization And Mapping ◽  
Camera Localization ◽  
New Feature ◽  
The Cost

We propose a real time indoor corridor layout estimation method based on visual Simultaneous Localization and Mapping (SLAM). The proposed method adopts the Manhattan World Assumption at indoor spaces and uses the detected single image straight line segments and their corresponding orthogonal vanishing points to improve the feature matching scheme in the adopted visual SLAM system. Using the proposed real time indoor corridor layout estimation method, the system is able to build an online sparse map of structural corner point features. The challenges presented by abrupt camera rotation in the 3D space are successfully handled through matching vanishing directions of consecutive video frames on the Gaussian sphere. Using the single image based indoor layout features for initializing the system, permitted the proposed method to perform real time layout estimation and camera localization in indoor corridor areas. For layout structural corner points matching, we adopted features which are invariant under scale, translation, and rotation. We proposed a new feature matching cost function which considers both local and global context information. The cost function consists of a unary term, which measures pixel to pixel orientation differences of the matched corners, and a binary term, which measures the amount of angle differences between directly connected layout corner features. We have performed the experiments on real scenes at York University campus buildings and the available RAWSEEDS dataset. The incoming results depict that the proposed method robustly performs along with producing very limited position and orientation errors.

Real-Time Nonlinear Programming by Amplitude Modulation

2008 ◽  
Author(s):  
Kyoungchul Kong ◽  
Kiyonori Inaba ◽  
Masayoshi Tomizuka
Keyword(s):  
Nonlinear Programming ◽  
Cost Function ◽  
Real Time ◽  
Amplitude Modulation ◽  
Extremum Seeking ◽  
Cost Functions ◽  
Variable Cost ◽  
The Cost ◽  
Variable Cost Function ◽  
Real Time Optimization

Nonlinear Programming (NLP) is for optimization of nonlinear cost functions. In applications of NLP for real-time optimization, however, the estimation of the gradient of the cost function remains as a challenge. On the other hand, the Extremum-Seeking Control (ESC) optimizes the cost function in real-time, but it involves a complicated design of filters in multi-dimensional cases. In this paper, a new method that optimizes an arbitrary multi-variable cost function in real-time is proposed. In the proposed method, the variables are updated as in NLP while the gradient of the cost function is continuously estimated by the amplitude modulation as in ESC. The proposed method does not require design of any complicated filters. The performance is verified by simulations on time-varying and noisy cost functions as well as automatic controller tuning applications.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2019 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2018 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

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