上下文感知的系统综合、任务分配与路由
Context-Aware System Synthesis, Task Assignment, and Routing
AI总结：
本文提出一种上下文感知的系统综合方法，将任务分配与路由形式化为整数线性规划，自动选择并组织组件以构建完整机器人系统，增强设计效率与韧性，并通过两个案例验证其表达力和可处理性。
摘要：
复杂机器人系统的设计与组织传统上需要耗费大量精力的试错过程，以确保硬件和软件组件都能正确连接并具备计算所需的资源。本文提出了一种对二次分配与路由问题的新颖推广，引入了用于选择组件和互连的正式化方法，以综合出一个能够提供用户定义功能的完整系统。通过将任务上下文、功能需求和模块化直接引入分配问题，我们推导出一种解决方案，其中组件被自动选择，然后组织成最优的硬件和软件互连结构，同时尊重组件可行性和所需功能的限制。直接从单个组件生成完整功能系统的能力减少了手动设计工作量，允许对设计空间进行引导式探索。此外，我们的公式化通过量化资源裕度并支持系统结构适应环境变化、硬件或软件故障来增强韧性。所提出的公式化被转化为一个整数线性规划，该规划可证明是$\mathcal{NP}$-难的。我们开发并分析了两个案例研究，以突出该方法所能处理问题的表达力和复杂性：第一个案例探讨了在多种任务背景下地面搜索与救援机器人的迭代开发，第二个案例则探讨了DARPA机器人挑战赛中仿人灾难机器人的大规模复杂设计。数值模拟量化了实际性能，并展示了在许多现代机器人系统中遇到的问题规模下，时间复杂度的可处理性。
英文摘要：
The design and organization of complex robotic systems traditionally requires laborious trial-and-error processes to ensure both hardware and software components are correctly connected with the resources necessary for computation. This paper presents a novel generalization of the quadratic assignment and routing problem, introducing formalisms for selecting components and interconnections to synthesize a complete system capable of providing some user-defined functionality. By introducing mission context, functional requirements, and modularity directly into the assignment problem, we derive a solution where components are automatically selected and then organized into an optimal hardware and software interconnection structure, all while respecting restrictions on component viability and required functionality. The ability to generate complete functional systems directly from individual components reduces manual design effort by allowing for a guided exploration of the design space. Additionally, our formulation increases resiliency by quantifying resource margins and enabling adaptation of system structure in response to changing environments, hardware or software failure. The proposed formulation is cast as an integer linear program which is provably $\mathcal{NP}$-hard. Two case studies are developed and analyzed to highlight the expressiveness and complexity of problems that can be addressed by this approach: the first explores the iterative development of a ground-based search-and-rescue robot in a variety of mission contexts, while the second explores the large-scale, complex design of a humanoid disaster robot for the DARPA Robotics Challenge. Numerical simulations quantify real world performance and demonstrate tractable time complexity for the scale of problems encountered in many modern robotic systems.
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