Special Issue on Real World Robot Challenge in Tsukuba – Autonomous Technology for Coexistence with Human Beings –

2016 ◽  
Vol 28 (4) ◽  
pp. 431-431 ◽  
Author(s):  
Yoshihiro Takita ◽  
◽  
Shin’ichi Yuta ◽  
Takashi Tsubouchi ◽  
Koichi Ozaki
Keyword(s):  
Real World ◽  
Control Technology ◽  
Human Beings ◽  
Technological Education ◽  
Second Stage ◽  
Advanced Control ◽  
Mobile Robot Control ◽  
Technological Challenge ◽  
World Environment ◽  
Autonomous Technology

This issue is the third in a series on Real World Robot Challenge in Tsukuba. The Tsukuba Challenge has contributed much in establishing autonomous mobile robot control technology on outdoor walkways where robots must mingle with pedestrians and cyclists – not all of whom may be familiar with such robots. The rain on the day of the final run for the 2015 Tsukuba Challenge taught us valuable lessons in navigating robots in real environments. Since the 2013 Tsukuba Challenge, a new task was introduced in the second stage. This task consists of searching for human targets – a technological challenge for developing robots that are both mobile and useful. Our objective here is to share advanced control technology refined through experiments in the real-world environment of the Tsukuba Challenge. The Tsukuba Challenge is also providing a forum for technological education for university students studying robotics engineering and for technical exchange through open experiments. In this issue, we are pleased to present the control technology that this exchange has brought about. We would like to express our deep gratitude to the authors contributing to this issue and to the article reviewers who have helped make this all possible.

Special Issue on Real World Robot Challenge in Tsukuba – Autonomous Technology for Useful Mobile Robot –

2015 ◽  
Vol 27 (4) ◽  
pp. 317-317 ◽  
Author(s):  
Yoshihiro Takita ◽  
Shin’ichi Yuta ◽  
Takashi Tsubouchi ◽  
Koichi Ozaki
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Real World ◽  
Autonomous Mobile Robots ◽  
Test Field ◽  
Human Beings ◽  
Special Issue ◽  
Problems And Solutions ◽  
Technological Challenge ◽  
Autonomous Technology

The first Tsukuba Challenge started in 2007 as a technological challenge for autonomous mobile robots moving around on city walkways. A task was then added involving the search for certain persons. In these and other ways, the challenge provides a test field for developing positive relationships between mobile robots and human beings. To make progress an autonomous robotic research, this special issue details and clarifies technological problems and solutions found by participants in the challenge. We sincerely thank the authors and reviewers for this chance to work with them in these important areas.

Special Issue on Real World Robot Challenge in Tsukuba – Autonomous Technology for Coexistence with Human Beings –

2017 ◽  
Vol 29 (4) ◽  
pp. 629-629 ◽  
Author(s):  
Yoshihiro Takita ◽  
Shin’ichi Yuta ◽  
Takashi Tsubouchi ◽  
Koichi Ozaki
Keyword(s):  
Real World ◽  
Control Method ◽  
Autonomous Robots ◽  
Weather Conditions ◽  
The Novel ◽  
Human Beings ◽  
Special Issue ◽  
Novel Technology ◽  
World Environment ◽  
Autonomous Technology

In this fourth of the “Special Issues on Real World Robot Challenge in Tsukuba,” we feature the control technology of autonomous robots. There is no guarantee that it will operate perfectly in a real-world environment even with the method already revealed. Participating robots in Tsukuba Challenge are required to carry out the assigned tasks under the prevailing weather conditions on the day of the events. Robots avoid oncoming pedestrians and obstacles in their path. In order to share the novel technology of the autonomous control method, this special issue presents a summary of the results of robots that participated in past Tsukuba Challenges. It is only thanks to the ongoing efforts of the organizers of Tsukuba Challenge and the enthusiasm on the part of the participants that we are able to present an issue such as this, and we are truly thankful to them. We also wish to thank the authors who submitted papers and articles for this issue, as well as our reviewers.

Real-world environment affects the color appearance of virtual stimuli produced by augmented reality

2019 ◽  
Vol 2019 (1) ◽  
pp. 237-242
Author(s):  
Siyuan Chen ◽  
Minchen Wei
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Mixed Reality ◽  
Rapid Development ◽  
Light Level ◽  
Color Appearance ◽  
Chromatic Adaptation ◽  
Lighting Condition ◽  
The Real ◽  
World Environment

Color appearance models have been extensively studied for characterizing and predicting the perceived color appearance of physical color stimuli under different viewing conditions. These stimuli are either surface colors reflecting illumination or self-luminous emitting radiations. With the rapid development of augmented reality (AR) and mixed reality (MR), it is critically important to understand how the color appearance of the objects that are produced by AR and MR are perceived, especially when these objects are overlaid on the real world. In this study, nine lighting conditions, with different correlated color temperature (CCT) levels and light levels, were created in a real-world environment. Under each lighting condition, human observers adjusted the color appearance of a virtual stimulus, which was overlaid on a real-world luminous environment, until it appeared the whitest. It was found that the CCT and light level of the real-world environment significantly affected the color appearance of the white stimulus, especially when the light level was high. Moreover, a lower degree of chromatic adaptation was found for viewing the virtual stimulus that was overlaid on the real world.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

scholarly journals Impact of Position and Orientation of RFID Tags on Real Time Asset Tracking in a Supply Chain

2008 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Sidney D’Mello ◽  
Eric Mathews ◽  
Lee McCauley ◽  
James Markham
Keyword(s):  
Supply Chain ◽  
Real World ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Rfid Tags ◽  
Vertical Orientation ◽  
Fine Grained ◽  
Asset Tracking ◽  
Transverse Electromagnetic ◽  
World Environment

We studied the characteristics of four commercially available RFID tags such as their orientation on an asset and their position in a three dimensional real world environment to obtain comprehensive data to substantiate a baseline for the use of RFID technology in a diverse supply chain management setting. Using RFID tags manufactured by four different vendors and a GHz Transverse Electromagnetic (GTEM) cell, in which an approximately constant electromagnetic (EM) field was maintained, we characterized the tags based on horizontal and vertical orientation on a simulated asset. With these baseline characteristics determined, we moved two of the four tags through a real world environment in three dimensions using an industrial robotic system to determine the effect of asset position in relation to the reader on tag readability. Combining the data collected over these two studies, we provide a rich analysis of the feasibility of asset tracking in a real world supply chain, where there would likely be multiple tag types. We offer fine grained analyses of the tag types and make recommendations for diverse supply chain asset tracking.

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

An autonomous excavator system for material loading tasks

2021 ◽  
Vol 6 (55) ◽  
pp. eabc3164
Author(s):  
Liangjun Zhang ◽  
Jinxin Zhao ◽  
Pinxin Long ◽  
Liyang Wang ◽  
Lingfeng Qian ◽  
...  
Keyword(s):  
Real World ◽  
Material Handling ◽  
Texture Classification ◽  
Detection Algorithms ◽  
Unstructured Environments ◽  
Ground Collapse ◽  
Dump Trucks ◽  
Multimodal Perception ◽  
World Environment ◽  
Indoor And Outdoor

Excavators are widely used for material handling applications in unstructured environments, including mining and construction. Operating excavators in a real-world environment can be challenging due to extreme conditions—such as rock sliding, ground collapse, or excessive dust—and can result in fatalities and injuries. Here, we present an autonomous excavator system (AES) for material loading tasks. Our system can handle different environments and uses an architecture that combines perception and planning. We fuse multimodal perception sensors, including LiDAR and cameras, along with advanced image enhancement, material and texture classification, and object detection algorithms. We also present hierarchical task and motion planning algorithms that combine learning-based techniques with optimization-based methods and are tightly integrated with the perception modules and the controller modules. We have evaluated AES performance on compact and standard excavators in many complex indoor and outdoor scenarios corresponding to material loading into dump trucks, waste material handling, rock capturing, pile removal, and trenching tasks. We demonstrate that our architecture improves the efficiency and autonomously handles different scenarios. AES has been deployed for real-world operations for long periods and can operate robustly in challenging scenarios. AES achieves 24 hours per intervention, i.e., the system can continuously operate for 24 hours without any human intervention. Moreover, the amount of material handled by AES per hour is closely equivalent to an experienced human operator.

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