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Biped wall-climbing robots well adapt for carrying out tasks in large-scale 3-D environments such as skeleton-facade structures, due to their good abilities to transit between walls and surmount obstacles. However, to plan 3-D collision-free climbing paths in such environments is challenging and significant for biped wall-climbing robots. In this article, we propose a novel hierarchical framework for planning 3-D biped climbing navigation, including three levels, namely the global route, the foothold, and the single-step motion. We also present three effective planners incorporated in the framework to solve this challenging planning problem efficiently. A global route planner is designed on top, for rapidly analyzing the possibility for the robot to transit between walls, and searching walls that must be passed through and key footholds for performing transition, in order to optimize the route length globally. A local foothold planner is implemented in the middle, to search and optimize footholds on each via wall, considering the robot's ability to step over, step on, and bypass obstacles. A single-step motion planner is deployed at the bottom, for planning collision-free movement swinging the robot suction module from a foothold to another. Simulations are conducted to fully verify the effectiveness and performance of the presented planners. Experiments with W-Climbot are carried out to demonstrate that the proposed framework and planners succeed in planning 3-D collision-free optimized climbing paths for biped wall-climbing robots. [ABSTRACT FROM AUTHOR]

Understanding the changes of time-series is a common task in many application domains. Converting time-series data into videos helps an audience with little or no background knowledge gain insights and deep impressions. It essentially integrates data visualizations and animations to present the evolution of data expressively. However, it remains challenging to create this kind of data video. First, it is difficult to efficiently detect important changes and include them in the video sequence. Existing methods require much manual effort to explore the data and find changes. Second, how these changes are emphasized in the videos is also worth studying. A video without emphasis will hinder an audience from noticing those important changes. This article presents an approach that extracts and visualizes important changes of a time-series. Users can explore and modify these changes, and apply visual effects on them. Case studies and user feedback demonstrate the effectiveness and usability of our approach. [ABSTRACT FROM AUTHOR]