Trajectory retiming is the task of computing a feasible time parameterization to traverse a path. It is commonly used in the decoupled approach to trajectory optimization whereby a path is first found, then a retiming algorithm computes a speed profile that satisfies kino-dynamic and other constraints. While trajectory retiming is most often formulated with the minimum-time objective (i.e. traverse the path as fast as possible), it is not always the most desirable objective, particularly when we seek to balance multiple objectives or when bang-bang control is unsuitable. In this paper, we present a novel algorithm based on factor graph variable elimination that can solve for the global optimum of the retiming problem with quadratic objectives as well (e.g. minimize control effort or match a nominal speed by minimizing squared error), which may extend to arbitrary objectives with iteration. Our work extends prior works, which find only solutions on the boundary of the feasible region, while maintaining the same linear time complexity from a single forward-backward pass. We experimentally demonstrate that (1) we achieve better real-world robot performance by using quadratic objectives in place of the minimum-time objective, and (2) our implementation is comparable or faster than state-of-the-art retiming algorithms.

Machine Learning leverages data science and natural language processing to understand and capture the non-linear relationships between different …

According to a new report by consultancy firm Accenture, corporate profitability is in decline across most industries in the United States – and is also impacting investment and output in public services. After reaching their highest share of national income in the post-war era, the growth of profits dropped from 25 percent in 2010 to -3 percent in 2015, the report states. It is not a rosy picture for the future if things carry on as they are. Indeed, the current data do not suggest an environment conducive to growth. Business investment is already close to stalling. For instance, in manufacturing business investment growth has declined from 14.8 percent in 2012 to -5.2 percent in 2016 in the United States and from 5.9 percent in 2012 to -6.6 percent in 2016 in the United Kingdom .

DALIAN, China--(BUSINESS WIRE)--New research from Accenture (NYSE:ACN) reveals that artificial intelligence (AI) could accelerate China's economic growth rate from 6.3 percent to 7.9 percent by 2035, by transforming the nature of work and opening new sources of value and growth. The report, titled "How Artificial Intelligence Can Drive China's Growth," explores new insights into AI and its impact on China's economy. Based on analysis and modeling by Accenture Research, in collaboration with Frontier Economics, there is dramatic impact on China's growth when AI is added as a completely new factor of production to the economic growth model. "China has already made great leaps in the development of AI and our research shows that it has the potential to be a powerful remedy for slowing growth," said Chuan Neo Chong, Accenture Greater China Chairwoman. "However, as with any catalyst, it is important to remember the challenges and the risk of unintended consequences. Stakeholders must prepare themselves intellectually, technologically, politically, ethically and socially for the promise of AI."

New research from Accenture (NYSE:ACN) reveals that artificial intelligence (AI) could accelerate China's economic growth rate from 6.3 percent to 7.9 percent by 2035, by transforming the nature of work and opening new sources of value and growth. This Smart News Release features multimedia. AI is poised to boost China's GVA by USD $7,111 billion by 2035 (Graphic: Business Wire) The report, titled "How Artificial Intelligence Can Drive China's Growth," explores new insights into AI and its impact on China's economy. Based on analysis and modeling by Accenture Research, in collaboration with Frontier Economics, there is dramatic impact on China's growth when AI is added as a completely new factor of production to the economic growth model. "China has already made great leaps in the development of AI and our research shows that it has the potential to be a powerful remedy for slowing growth," said Chuan Neo Chong, Accenture Greater China Chairwoman.