CAREER: Generalizations in Obstacle Avoidance Theory

职业：避障理论的概括

• 批准号: 1851817
• 负责人: Animesh Chakravarthy
• 金额: $ 35.7万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1851817&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，使避障指导法律的分析表征，伴随着这些法律的实验验证。 这具有重要意义，因为避障问题是路径规划问题的重要组成部分，路径规划问题出现在几个不同的领域，包括机器人技术，自主空中，地面和水下车辆，计算机动画，分子运动，自主轮椅，避免空间碎片的航天器，机器人手术，盲人辅助设备，所设计的制导律特别适用于在诸如包含机器人操纵器、类人机器人编队飞行的车辆和其他高维空间，其中智能体没有关于其环境的先验信息。 的鲁棒性分析所设计的制导律的各种不确定性，如传感器噪声，数据延迟和数据丢失进行，然后通过实验验证，其中的制导律被编码在微控制器平台上的资源有效的方式和小型机器人地面和空中车辆上实施。 预期的结果包括指导法律适用于避免碰撞的障碍物的各种，可能随时间变化，形状移动在高维随机环境中，沿着假设这些指导法律的安全保证。 该项目还开展了多项推广活动，并引入了新的课程，以促进机器人技术的教育和应用，这些活动从K-12开始一直到本科和研究生阶段的工程教育。