Adaptive and Efficient Robot Positioning Through Model and Task Fusion

通过模型和任务融合实现自适应且高效的机器人定位

• 批准号: DE240100149
• 负责人: Dr Tobias Fischer
• 金额: $ 32.04万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-11-01 至 2027-10-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE240100149
• 关键词: Adaptive; Efficient; Robot; Positioning; Through

This project aims to create fit-for-purpose positioning systems that continuously adapt to diverse and changing environments. The project expects to contribute to the knowledge across robotics, computer vision, and neuromorphic computing. Expected outcomes of this project include ground-breaking place recognition techniques that address two fundamental limitations in the state-of-the-art: continuous adaptation, critically important in safety-critical systems, and energy efficiency, critically important in resource-constrained systems. This should provide significant benefits, such as accelerated deployment of mobile robots, drones and augmented reality solutions in manufacturing, defence, healthcare, household, and space.

该项目旨在创建适合用途的定位系统，以不断适应各种环境和不断变化的环境。该项目希望有助于跨机器人技术，计算机视觉和神经形态计算的知识。该项目的预期结果包括开创性的地点识别技术，这些技术解决了最新的两个基本局限性：持续适应，在安全至关重要的系统中至关重要，并且在资源约束系统中至关重要。这将提供巨大的好处，例如在制造，国防，医疗保健，家庭和空间中加速移动机器人，无人机和增强现实解决方案的部署。