CAREER: Generalizations in Obstacle Avoidance Theory

职业：避障理论的概括

• 批准号: 1351677
• 负责人: Animesh Chakravarthy
• 金额: $ 40万
• 依托单位: Wichita State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-15 至 2018-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1351677&HistoricalAwards=false
• 关键词: CAREER; Generalizations; Obstacle; Avoidance; Theory

This project develops a theoretical framework that enables an analytical characterization of guidance laws for obstacle avoidance, accompanied by an experimental validation of these laws. This has significant implications since the obstacle avoidance problem is an important component of the path planning problem, which appears in several diverse fields including robotics, autonomous air, ground and underwater vehicles, computer animation, molecular motion, autonomous wheelchairs, spacecraft avoiding space debris, robotic surgery, assistance aids for the blind, etc. The guidance laws designed are particularly applicable for real-time implementation of precise path planning in cluttered dynamic environments such as those containing robot manipulators, humanoid robots, vehicles flying in formation and other high-dimensional spaces wherein the agents have no a priori information about their environment. A robustness analysis of the designed guidance laws to various uncertainties such as sensor noise, data delays and data dropouts is performed, followed by an experimental validation wherein the guidance laws are coded on microcontroller platforms in a resource-efficient manner and implemented on small-scale robotic ground and air vehicles. The expected results include guidance laws suitable for collision avoidance of obstacles of various, possibly time-varying, shapes moving in high-dimensional stochastic environments, along with a postulation of the safety guarantees of these guidance laws. This project also performs multiple outreach activities and introduces new curriculum that promote the education and applications of robotics, and these activities are conducted in levels starting from K-12 all the way through undergraduate and graduate level engineering education.

该项目开发了一个理论框架，该框架能够对避免障碍的指导定律进行分析表征，并伴随着对这些法律的实验验证。 这具有重大意义，因为避免障碍问题是路径计划问题的重要组成部分，该问题出现在包括机器人技术，自主空气，地面和水下车辆，计算机动画，分子运动，自主轮椅，自主轮椅，避免太空垃圾，机器人手术，机器人范围实施的盲目策略等方向的动态范围内的几个不同领域，包括机器人，自主空气，地面和水下车辆，分子运动，自主轮椅，航天器，避免了空间，该航天措施是在盲人方面进行的。作为包含机器人操纵器，人形机器人，车辆在编队中飞行的车辆和其他高维空间的人，其中代理没有关于其环境的先验信息。 对各种不确定性（例如传感器噪声，数据延迟和数据辍学）进行了对设计的指导法律的鲁棒性分析，然后进行实验验证，其中指导法在微控制器平台上以资源效率的方式进行编码，并以小规模的机器人地面和机动车辆进行实施。 预期的结果包括适用于避免碰撞的指导法律，以在高维随机环境中移动的各种障碍，以及对这些指导法律的安全保证的假设。 该项目还进行了多项外展活动，并介绍了促进机器人技术的教育和应用的新课程，并且这些活动的级别从K-12一直到本科和研究生水平的工程教育开始。