CSR: Medium: Attention-Driven Software Architecture for Integrated Perception and Planning in Autonomous Agents

CSR：中：用于自主代理中集成感知和规划的注意力驱动软件架构

• 批准号: 2312395
• 负责人: Hyoseung Kim
• 金额: $ 120万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312395&HistoricalAwards=false
• 关键词: CSR; Medium; Attention; Driven; Software

While significant progress has been made in developing autonomous systems, effectively processing and making sense of vast amounts of sensor data in real time remains a challenge, especially for small robotic agents with limited computing capabilities. To address this problem, this project will create an attention-driven software architecture that can identify and prioritize critical information from sensors, enabling timely decision-making while considering resource constraints and uncertainties in the environment. This architecture will holistically optimize computation scheduling, perception, and planning by adapting to the context and anticipate future actions. Three important advances to be made in this project are: (1) context adaptive scheduling of autonomous computation pipelines, (2) learning-based perception to anticipate future actions in dynamic environments, and (3) motion planning and decision making based on anticipated actions in the presence of uncertainty. By interrelating these components through the attention-driven architecture, this project will tackle fundamental challenges associated with time-sensitive scenarios in resource-constrained autonomous systems. This project will have broader impacts across various domains, including smart agriculture, manufacturing, surveillance, and robotic delivery. The proposed system-theoretic research will empower resource-constrained robotic agents with safe, reliable, and real-time autonomous capabilities in rapidly changing environments. The project plans to establish educational programs and engage with industry partners to provide undergraduate research opportunities. By actively involving the broader community and targeting underrepresented students, this research will cultivate a talented and diverse workforce for the future of autonomous systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管在开发自主系统方面已经取得了重大进展，但实时处理大量传感器数据仍然是一个挑战，尤其是对于具有有限的计算能力的小型机器人代理仍然是一个挑战。为了解决此问题，该项目将创建一个以注意力为导致的软件体系结构，该软件体系结构可以从传感器中识别和确定关键信息，从而及时决策，同时考虑环境中的资源约束和不确定性。该体系结构将通过适应上下文并预测未来的动作来整体优化计算计划，感知和计划。该项目要取得的三个重要进展是：（1）自主计算管道的自适应安排，（2）基于学习的感知，以预测动态环境中未来的行动，以及（3）在不确定性面前基于预期的行动的运动计划和决策。通过通过注意力驱动的体系结构将这些组件相关联，该项目将解决与资源受限自主系统中时间敏感的情况相关的基本挑战。该项目将对各个领域产生更大的影响，包括智能农业，制造，监视和机器人交付。拟议的系统理论研究将使在迅速变化的环境中具有安全，可靠和实时的自主功能，使资源受到的机器人能力能力。该项目计划建立教育计划并与行业合作伙伴互动，以提供本科研究机会。通过积极参与更广泛的社区并针对代表性不足的学生，这项研究将为自主系统的未来培养一个有才华和多样化的劳动力。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。