Hybrid systems, in which digital and analogue devices and sensors interact over time, is attracting the attention of researchers. Representation of states and the physical system condition includes continuous and discrete numerics, in addition to symbols and logical parameters. Most of the current vision and robotics problems, as well as problems in other domains, fall within the description of hybrid systems. There as many issues that need to be resolved, among them, definitions for observability, stability and stabilizability, controllability in general, uncertainty of state transitions and identification of the system. The general observation problem falls within the hybrid system domain, as there is a need to report, observe and control distinct and discrete system states. There is also a need for recognizing continuous 2-D and 3-D evolution of parameters. Also, there should be a symbolic description of the current state of the system, especially in the manipulation domain.
We do not intend to give a solution for the problem of defining, monitoring or controlling such hybrid systems in general. What we intend to present in this work is a framework that works for the class of hybrid systems encountered within the robotic observation paradigm. The representation we advocate allows for the symbolic and numeric, continuous and discrete aspects of the observation task. We conjecture that the framework could be explored further as a possible basis for providing solutions for general hybrid systems representation and analysis problems.
We suggest the use of a representation of discrete event dynamic systems, which is augmented by the use of a concrete definition for the events that causes state transitions, within the observation domain. We also use some uncertainty modeling to achieve robustness and smoothness in asserting state and continuous event variations over time.
Dynamic systems are sometimes modeled by finite state automata with partially observable events together with a mechanism for enabling and disabling a subset of state transitions [19,22,23], the reader is referred to those references for more information about this class of DEDS representation. We propose that such a DEDS skeleton is a suitable high-level framework for many vision and robotics tasks, in particular, we use the DEDS model as a high-level structuring technique for a system to observe a robot hand manipulating an object.