Physics-based encapsulation in embedded software for distributed sensing and control applications

Spatial Aggregation abstracts data arising from distributed embedded sensing and control applications as a set of so-called spatio-temporal objects. Locality and continuity in the underlying physics of a problem domain give rise to spatially coherent and temporally contiguous objects in an appropriate metric space. Once parameterized by physical properties such as location, intensity (e.g., light, temperature, pressure), and motion (e.g., velocity), these objects can be aggregated and abstracted into more abstract descriptions. Applications are written as the creation and transformation of these abstract objects. We illustrate how these objects naturally arise from applications such as distributed sensing and actuation, and use an air-jet table system to demonstrate how such a physics-based encapsulation modularizes the design of sensing and control software. Unlike in traditional software design, where objects and operations are defined mathematically and possess a semantics independent of possible implementations, the objects in distributed embedded software are defined by the physics of the application, algorithmic considerations, and task requirements, as well as optimization criteria. The air-jet table example demonstrates that the grouping and abstraction of actuation devices are determined by laws of motion, the type of force allocation algorithms used, and the desired performance of the controller; this encapsulation greatly simplifies the design and implementation of a force allocation algorithm for the system and improves software modularity. Based on our practical experiences in designing several massively distributed sensing and actuation systems, we present a set of recommendations for distributed embedded software modeling and design.

[1]  Feng Zhao,et al.  Imagistic reasoning , 1995, CSUR.

[2]  Feng Zhao,et al.  Spatial Aggregation: Theory and Applications , 1996, J. Artif. Intell. Res..

[3]  Chris Bailey-Kellogg,et al.  Influence-based model decomposition for reasoning about spatially distributed physical systems , 2001, Artif. Intell..

[4]  Leo Joskowicz,et al.  Computational Kinematics , 1991, Artif. Intell..

[5]  W. B. Jackson,et al.  Constrained optimization based control of real time large-scale systems: airjet object movement system , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[6]  David B. Leake Artiicial Intelligence , 2001 .

[7]  Feng Zhao,et al.  Relation-based aggregation: finding objects in large spatial datasets , 2000, Intell. Data Anal..

[8]  James R. Munkres,et al.  Elements of algebraic topology , 1984 .

[9]  Gerald J. Sussman,et al.  Structure and interpretation of computer programs , 1985, Proceedings of the IEEE.

[10]  Kenneth Yip Structural Inferences from Massive Datasets , 1997, IJCAI.

[11]  Feng Zhao,et al.  Extracting and Representing Qualitative Behaviors of Complex Systems in Phase Spaces , 1991, IJCAI.

[12]  Ranjit Karmakar,et al.  Modeling and Simulation of Engineering Systems Through Bondgraphs , 1999 .

[13]  Chris Bailey-Kellogg,et al.  Spatial Aggregation: Language and Applications , 1996, AAAI/IAAI, Vol. 1.

[14]  Michael Tiller,et al.  Introduction to Physical Modeling with Modelica , 2001 .

[15]  Chris Bailey-Kellogg,et al.  Influence-Based Model Decomposition , 1999, AAAI/IAAI.

[16]  Adam Huang,et al.  MEMS transducers for aerodynamics - A paradigm shift , 2000 .

[17]  F. Zhao,et al.  Finding Structures in Weather Maps , 1998 .

[18]  Feng Zhao,et al.  Intelligent Simulation in Designing Complex Dynamic Control Systems , 1995 .

[19]  Markus P. J. Fromherz,et al.  Force allocation in a large-scale distributed active surface , 2003, IEEE Trans. Control. Syst. Technol..