CISE Research Infrastructure: Asymmetric Bandwidth Channels: Applications to Real-Time Computing and Robotics

Bajcsy， Ruzena宾夕法尼亚大学非对称带宽通道：实时计算和机器人的应用该奖项是为了获得基础设施，以支持研究一种具有成本效益和广泛部署的通信模型，非对称带宽通道，在计算机科学中很少有抽象可用。研究以客户端和服务器之间的低带宽交互通道为特征的系统的应用，以及从服务器到客户端的高带宽广播。建议的工作将开发应用程序可以有效地用于这种基础设施的通信抽象，以及这些抽象的计算模型。然后将在多个半自主机器人代理的测试平台上评估模型的性能。在这个项目中需要解决的具有挑战性的问题包括：(1)选择从服务器到客户端的路径；(2)确定在计算机通信环境中广播信道共享的可能程度；(3)调度共享广播终端的传输。研究还将针对机器人智能体协调的三个基本问题：(1)基于个体智能体的观察和对未知或部分已知环境的探索，建立一个完整的模型；(2)基于世界模型的任务规划，同时考虑可能存在的不确定性和延迟；(3)基于视觉和其他感官信息的机器人代理控制。

CDA-9703220 Bajcsy, Ruzena University of Pennsylvania Asymmetric Bandwidth Channels: Applications to Real-Time Computing and Robotics This award is for the acquisition of infrastructure to support research which is to investigate a cost-effective and broadly deployed communication model, Asymmetric Bandwidth Channels, for which few abstractions in computer science are available. Research into the application of systems characterized by low-bandwidth interactive channels between clients and server, and a high-bandwidth broadcast from server to clients will be conducted. The proposed work will develop communications abstractions that applications can effectively use for such an infrastructure, and computational models for these abstractions. Model performance will then be evaluated on a testbed of multiple semi-autonomous robotic agents. The challenging problems to be addressed in this project include: (1) selecting which path to take from server to client; (2) determining the degree of broadcast channel sharing possible in a computer communications environment; and (3) scheduling transmissions from the shared broadcast terminal. The research will also target three fundamental problems underlying coordination of robotic agents: (1) development of world models based on observations of individual agents and exploration of an unknown or a partially known environment to build a complete model; (2) task planning based on the world model while accounting for possible uncertainties and latencies; and (3) control of robotic agents based on visual and other sensory information.