A discrete event framework for autonomous observation under uncertainty (original) (raw)

In this work we establish a framework for the general problem of observation, which may be applied to different kinds of visual tasks. We construct ‘intelligent’ high-level control mechanisms for active visual recognition of different processes within a hybrid dynamic system. We address the problem of observing a manipulation process in order to illustrate the ideas and motive behind our framework. We use a discrete event dynamic system as a high-level structuring technique to model the manipulation system. The formulation utilizes the knowledge about the system and the different actions in order to solve the observer problem in an efficient, stable and practical manner. The model uses different tracking mechanisms so that the observer can ‘see’ the workspace of the manipulating robot. An automaton is developed for the hand/object interaction over time and a stabilizing observer is constructed. Low-level modules are developed for recognizing the visual ‘events’ that causes state transitions within the dynamic manipulation system in real time. A coarse quantization of the manipulation actions is used in order to attain an active, adaptive and goaldirected sensing mechanism. The formulation provides high-level symbolic interpretations of the scene under observation. The discrete event framework is augmented with mechanisms for recovering the continuous parametric evolution of the scene under observation and for asserting the state of the manipulation agent. This work examines closely the possibilities for errors, mistakes and uncertainties in the manipulation system, observer construction process and event identification mechanisms. We identify and suggest techniques for modeling these uncertainties. Ambiguities are allowed to develop and are resolved after finite time. Error recovery mechanisms are also devised. The computed uncertainties are utilized for navigating the observer automaton state space, asserting state transitions and developing a suitable tracking

Autonomous Observation Under Uncertainty

1992

We address the problem of observing a moving agent. They advocate a modeling approach for the visual system and its observer, where a discrete event dynamic system framework is developed and events are defined as ranges on parameter subsets. A system for observing a manipulation process in which a robot hand manipulates an object is proposed. The hand/object interaction is recognized over time, and a stabilizing observer is constructed. Low-level modules are developed for recognizing the events that cause state transitions within the dynamic manipulation system. The work examines closely the possibilities for errors, mistakes, and uncertainties in the manipulation system, observer construction process, and even identification mechanisms. The system utilized different tracking techniques to observe and recognize the task in an active and goal-directed manner

A Model for Visual Observation Under Uncertainty

1992

We address the problem of observing a moving agent is addressed. A modeling approach for the visual system and its observer is advocated in which a discrete-event dynamic system (DEDS) framework is developed and events are defined as ranges on parameter subsets. In particular, a system for observing a process in which a robot hand manipulates an object is proposed. A stabilizing observer is constructed, and low-level modules are developed for recognizing the events that cause state transitions within the dynamic manipulation system. The work examines closely the possibilities for errors and uncertainties in the manipulation system, observer construction process, and event identification mechanisms. The system utilizes different tracking techniques to observe and recognize the task in an active, adaptive, and goal-directed manner

Visual Observation of A Moving Agent

1991

We address the problem of observing a moving agent. In particular, we propose a system for observing a manipulation process, where a robot hand manipulates an object. A discrete event dynamic system (DEDS) frame work is developed for the hand/object interaction over time and a stabilizing observer is constructed. Low-level modules are developed for recognizing the “events” that causes state transitions within the dynamic manipulation system. The work examines closely the possibilities for errors, mistakes and uncertainties in the manipulation system, observer construction process and event identification mechanisms. The system utilizes different tracking techniques in order to observe and recognize the task in an active, adaptive and goal-directed manner.

Object Position and Orientation Tracking for Manipulators Under Sensing Dynamics

— Real-time object tracking for manipulators has been applied to industrial automation such as assembly and human robot collaboration. During the tracking process, the vision sensor is used as a feedback to the robot controller. It means that the imperfect sensing and stability issues must be carefully considered. This paper deals with the sensing dynamics (e.g. limited sampling rate, irregular sampling time, packet loss, noise and latency), and realizes globally asymptotically stable tracking. First, an irregular time Kalman filter is used to estimate the markers' positions and the corresponding error covariances. Then, a maximum likelihood estimation problem is solved to estimate the transformation between the target frame and the world frame. In addition, a dynamic tracking controller is implemented to realize object tracking. The stability of the tracking controller under model uncertainties is also discussed. The proposed tracking algorithm is experimentally verified on an industrial robot.

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