In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a “lazy” dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds—the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order O(n−1/d+ρ), where n is the number of sampled points, d is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive.
Background: Our study aimed to determine source of upload and content portrayed in the100 most-viewed videos on autism spectrum disorders (ASDs) on the video sharing public forum, YouTube. ASDs have become highly prevalent in the last decade, arousing a significant response from the media and psycho-educational health professions. Utilization of and reliance on social media for information on health matters has also proliferated. Some suggest that online videos could promote early detection (and intervention) of ASD by prompting caregivers to seek guidance. However, the usefulness of the available videos is unclear.Methods: The 100 most popular YouTube videos were examined for source of upload and information provided. Popularity was determined by number of views, using the filter tool.Results: The videos had more than 121 million views combined. Only one video had been uploaded by a professional (a clinical psychologist). The 99 (non-professional) videos provided minimal data and research into known ASD risk factors. Interestingly, discredited vaccine-associated risks were promoted in 16% (95% CI = 09%–25%) of the 100 videos analyzed. Many videos featured a child with ASD exhibiting some characteristic patterns, such as engaging in a repetitive behavior (73%, 95% CI = 63%-81%); about as many videos referenced various therapies (75%, 95% CI = 65%-83%); and 54% (95% CI = 44%–64%) and 61% (95% CI =51%–71%) of the videos mentioned the economic and emotional toll of ASD on the family,respectively. Additional variables are discussed.Conclusion: The most popular online videos were primarily uploaded by non-professionals and provided limited content regarding ASD. Given the wide reach of social media and its potential for providing valuable information and guidance to the public on matters pertaining to ASD, we wish to underscore the necessity for a professional presence in this medium.
The Internet is increasingly being viewed as a health promotion tool with enormous potential. However, this potential cannot be realized if Web sites do not utilize the features that make the Internet a "hybrid" mass and interpersonal communication medium. The purpose of this study was to examine interactive safer sex Web sites on a number of dimensions. A comprehensive search that included Internet search engines, links from well-known sites, and previously published reviews yielded 21 Web sites that met criteria. Web sites were coded on dimensions including targeting of the Web sites, safer sex messages presented, theoretical strategies utilized, interactivity, and other characteristics. Results indicate that a moderate amount of targeting of Web sites exists, especially on age group (e.g., teenagers); the most prevalent safer sex messages were to "use condoms" and "be sexually abstinent"; raising the perceived threat of sexually transmitted diseases and HIV was the most prevalent theoretical strategy used to motivate safer sex; and finally, a moderate amount of interactivity was found on the Web sites, with most Web sites containing 4 or 5 features out of 15 features examined. Evidence that Web sites were tailoring information or messages to individuals was not found. Implications of these results for improving safer sex Web sites and developing interventions online are discussed.
Assessing reachability for a dynamical system, that is deciding whether a certain state is reachable from a given initial state within a given cost threshold, is a central concept in controls, robotics, and optimization. Direct approaches to assess reachability involve the solution to a two-point boundary value problem (2PBVP) between a pair of states. Alternative, indirect approaches involve the characterization of reachable sets as level sets of the value function of an appropriate optimal control problem. Both methods solve the problem accurately, but are computationally intensive and do no appear amenable to real-time implementation for all but the simplest cases. In this work, we leverage machine learning techniques to devise querybased algorithms for the approximate, yet real-time solution of the reachability problem. Specifically, we show that with a training set of pre-solved 2PBVP problems, one can accurately classify the cost-reachable sets of a differentially-constrained system using either (1) locally-weighted linear regression or (2) support vector machines. This novel, query-based approach is demonstrated on two systems: the Dubins car and a deepspace spacecraft. Classification errors on the order of 10% (and often significantly less) are achieved with average execution times on the order of milliseconds, representing 4 orders-ofmagnitude improvement over exact methods. The proposed algorithms could find application in a variety of time-critical robotic applications, where the driving factor is computation time rather than optimality. the
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