Adaptive Crawler Research Articles

Design and analysis of a passive adaptive wall-climbing robot on variable curvature ship facades

To improve the adaptive motion performance of traditional wall-climbing robots on variable-curvature facades, a crawler-type wall-climbing robot suitable for ship wall features is proposed by utilizing the advantages of passive mechanisms in realizing autonomous robots. The robot consists of two passive adaptive crawler mechanisms and a connecting module. Each track structure contains multiple permanent magnets that can passively adapt to concave and convex facades of different curvatures. A static failure model is established according to the characteristics of the triangular distributed load, and the minimum adsorption force required for the robot to achieve safe motion is determined. The Halbach Array magnetic circuit design method was used to construct a gap-type permanent magnet adsorption model for lightweight design. The influence of wall thickness and air gap distance on the adsorption force is analyzed by parametric simulation. The prototype platform test shows that the robot can realize adaptive variable curvature motion through passive adjustment of the mobile mechanism attitude and has a certain load capacity.

Interaction with Obstacles During Pushing-Up Sequence for Ground Adaptive Crawler Robot

Interaction with Obstacles During Pushing-Up Sequence for Ground Adaptive Crawler Robot

Influential Factors for Ground Adaptability of Active Adaptive Crawler Robot

Influential Factors for Ground Adaptability of Active Adaptive Crawler Robot

Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling

In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM) framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The novel combination of our proposed adaptive crawler and our stream division/recombination technique provides significant gains in event recall (44.44%) and event precision (9.57%). The addition of these sub-events or pieces, allows us to get closer to solving the event puzzle.

対地適応クローラロボットの不整地走破時におけるサブクローラ回転軸コンプライアンスに関する考察

対地適応クローラロボットの不整地走破時におけるサブクローラ回転軸コンプライアンスに関する考察

Inference in OSNs via Lightweight Partial Crawls

Are Online Social Network (OSN) A users more likely to form friendships with those with similar attributes? Do users at an OSN B score content more favorably than OSN C users? Such questions frequently arise in the context of Social Network Analysis (SNA) but often crawling an OSN network via its Application Programming Interface (API) is the only way to gather data from a third party. To date, these partial API crawls are the majority of public datasets and the synonym of lack of statistical guarantees in incomplete-data comparisons, severely limiting SNA research progress. Using regenerative properties of the random walks, we propose estimation techniques based on short crawls that have proven statistical guarantees. Moreover, our short crawls can be implemented in massively distributed algorithms. We also provide an adaptive crawler that makes our method parameter-free, significantly improving our statistical guarantees. We then derive the Bayesian approximation of the posterior of the estimates, and in addition, obtain an estimator for the expected value of node and edge statistics in an equivalent configuration model or Chung-Lu random graph model of the given network (where nodes are connected randomly) and use it as a basis for testing null hypotheses. The theoretical results are supported with simulations on a variety of real-world networks.

受動適応クローラロボットにおけるサブクローラ角度と段差踏破の関係

受動適応クローラロボットにおけるサブクローラ角度と段差踏破の関係

1A1-U05 階段踏破時におけるマニピュレータ姿勢角を考慮した受動適応クローラロボットの重心位置変化

1A1-U05 階段踏破時におけるマニピュレータ姿勢角を考慮した受動適応クローラロボットの重心位置変化

Adaptive crawler for external hyperlinks search and acquisition

We describe a search robot (crawler) intended to collect information regarding outgoing hyperlinks from a given set of web sites related to a certain topic. The crawler's adaptive behavior is formulated in terms of a multi-armed bandit problem. Our experiments show that the choice of an adaptive algorithm for the crawler's rational behavior depends on the actual topic of the underlying set of web sites.

An Adaptive Topic Crawler for Electronic Public Opinion

Topic crawler is a tool for collecting electronic public opinion from the internet. The identification method of topics relevance identification directly affects the acquisition rate of topic crawler. To improve the low information acquisition rate of existing topic crawlers strategy, a modified SVM classifier algorithm which is based on online incremental learning is proposed. The idea of algorithm is to remove samples that affect the training set greatly in the historical training set, and then to re-train the historical set and the incremental set to obtain a complete training set. A framework of topic crawler is constructed on the basis of this algorithm. The results of experiments show that, this method can effectively improve the acquisition rate of the crawler.

Mobile robot with transformable crawler and intelligent guidance

Recently, great advances have been made in intelligent mobile robot technology, advances which will provide autonomous travelling ability to robots, allowing them to surmount stairs and other obstacles. In this paper, we present an active adaptive crawler mechanism and a visual navigating system, developed to achieve practical speed for industrial use of the mobile robot. By using a crawler-type mechanism, the active adaptive suspension mechanism maintains vehicle stability and achieves good climbing capability without increasing weight. Further, we have developed technology for an intelligent navigating mechanism, to guide the robot as it passes through a building. Once the robot is provided with an inner layout plan of the respective building, it can achieve practical mobile speed for industrial use.