This paper describes problems, challenges, and opportunities for intelligent simulation of physical systems. Prototype intelligent simulation tools have been constructed for interpreting massive data sets from physical fields and for designing engineering systems. We identify the characteristics of intelligent simulation and describe several concrete application examples. These applications, which include weather data interpretation, distributed control optimization, and spatio-temporal diffusion-reaction pattern analysis, demonstrate that intelligent simulation tools are indispensable for the rapid prototyping of application programs in many challenging scientific and engineering domains.

We describe a one-joint planar arm whichrepeatedly throws and catches parts on its surface, and we demonstrate that proper choice of the throwvelocityand arm geometry guarantees that the part will enter a unique recurrent motion pattern from a large set of initial configurations. The resulting system resembles an open-loop stable juggler of polygonal parts. Combined with a simple one-bit sensor, the system can be used as a parts feeder.

Part assembly is an important goal of part manipulation. Among other techniques, programmable force fields have been introduced for part manipulation. Many different force fields have been proposed to manipulate a part, such as the squeeze and the elliptical fields. For part assembly, more than one part needs to be manipulated. This can create problems since there can be interactions between the parts such as impact and friction. With technology developing, certain fields can be implemented in the micro-scale with MEMS actuator arrays and in the macro-scale with arrays of directed air jets or small motors. Modern technology is beginning to provide the means to control the magnitude and frequency of each actuator of the implemented force field. Thus dynamic and localized force fields can be used for part manipulation. This paper presents a novel strategy to assemble two parts with a sequence of static and dynamic programmable force fields. The strategy involves some initial sensing. Uncertainties occurring in the motion of the parts are taken into account to make the proposed strategy more robust.

Abstract. Robotics researchers will be aware of Dexter Kozen’s contributions to algebraic algorithms, which have enabled the widespread use of the theory of real closed fields and polynomial arithmetic for motion planning. However, Dexter has also made several important contributions to the theory of information invariants, and produced some of the most profound results in this field. These are first embodied in his 1978 paper On the Power of the Compass, with Manuel Blum. This work has had a wide impact in robotics and nanoscience. Starting with Dexter’s insights, robotics researchers have explored the problem of determining the information requirements to perform robot tasks, using the concept of information invariants. This represents an attempt to characterize a family of complicated and subtle issues concerned with measuring robot task complexity. In this vein, several measures have been proposed [14] to measure the information complexity of a task: (a) How much internal state should the robot retain? (b) How many cooperating robots are required, and how much communication between them is necessary? (c) How can the robot change (side-effect) the environment in order to record state or sensory information to perform a task? (d) How much information is provided by sensors? and (e) How much computation is required by the robot? We have considered how one might develop a kind of “calculus” on (a) – (e) in order to compare the power of sensor systems analytically. To this end, information invariants is a theory whereby one sensor can be “reduced ” to another (much in the spirit of computation-theoretic reductions), by adding, deleting, and reallocating (a) – (e) among collaborating autonomous robots. As we show below, this work steers using Dexter’s compass. 1 The Power of the Compass