机器人学是一门综合性学科,领域广、内容多,研究方向涉及机器人本体机构、传感器设计、信号处理、控制策略、人机交互、多机协作等。 机器人(Robot)是自动执行工作的机器装置。它既可以接受人类指挥,又可以运行预先编排的程序,也可以根据以人工智能技术制定的原则纲领行动。它的任务是协助或取代人类工作的工作,例如生产业、建筑业,或是危险的工作。

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总结题目

2019机器学习大总结,机器学习和机器人:我的2019年领域状态

总结简介

每年年底,我都喜欢回顾一下最能激发我灵感的各种潮流或报纸。作为这一领域的研究人员,我发现深入研究我认为研究界取得了令人惊讶的进展的地方,或者找出我们可能出乎意料地没有取得进展的领域,是相当有成效的。 在这里,我希望给出我对这个领域现状的看法。这篇文章无疑将是一个有偏见的样本,我认为这是该领域的进展。正如Jeff Dean所指出的,不仅仅是有效地覆盖了所有不可能的事情,每天大约有100篇机器学习论文发布到机器学习ArXiv!但是我对什么是进步的看法可能与你的不同。希望你们所有的读者都能从这篇文章中收集到一些东西,或者看到一篇你没听说过的论文。更好的是,你可以随意提出异议:我想进一步讨论我的想法,并在下面的评论或黑客新闻中听到其他观点。

总结内容

  • 从AlphaZero到MuZero
  • 表征学习(符号AI万岁)
  • 监督式计算机视觉研究降温(有所降温)
  • 成熟的技术
  • 图神经网络
  • 可解释AI
  • 仿真工具的持续增长和从模拟到真实的进展
  • 苦乐参半的教训

总结

随着2019年取得的进展,未来几年仍有成熟的增长领域。我希望看到更多的应用到部分可观察的领域,这需要一个代理对其环境有深入的了解,以便它可以对未来做出预测(这是我正在积极努力的事情)。我也有兴趣在所谓的long-lived AI(长寿人工智能)中看到更多的进展:随着他们花更多时间与周围环境互动,系统会不断学习和成长。目前,许多与世界互动的系统都很难优雅地处理噪音,除了最简单的应用程序外,大多数学习的模型都会随着传感器观测数量的增加而崩溃。

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We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the UFES's car, IARA. Finally, we list prominent autonomous research cars developed by technology companies and reported in the media.

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This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.

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This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.

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