Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Using control and vision for space applications

In this paper, we present algorithms that address the real-time robotic visual tracking (eye-in-hand configuration) of satellites that move in 2-D space. To achieve our objective, we combine computer vision techniques, for detection of motion, with simple control strategies. The problem has been formulated from the systems theory point of view. This faalitates the extension of the algorithm to 3-D spaae. A uoss-oorrelation technique (SSD optical flow) is uscd for computing the vector of discrete displacements and is combined with an appropriate control scheme to calculate the required motion of the space robotic system. Shared and traded control modes enable the integration of the human operator with the autonomous track-ing modules. In this way, the human operator can intervene and correct the tracking motion through a pystick. The perfor-mance of the propOSed algorithms has been tested on a real system. the CMU DD Arm 11, and the results are presented in this paper. 1

REAL-TIME LQG ROBOTIC VISUAL TRACKING X-

In this paper, a modified LQG technique is proposed for the solution of the robotic visual tracking problem (eye-in-hand configuration). The problem of mbotic visual tracking is formulated as a problem of combining control with computer vision. A crosstorrelation method provides the object's motion measurements which are used to update the system's measurement vector. These measurements arc fed to a discrete steady state Kalman filter that calculates the estimated values of the system's states and of the exogenous disturbances. Then, a discrete LQG controller computes the desired motion of the robotic system. Experimental results are presented to show the effectiveness of the approach. 1

Robotic Visual Servoing Around a Static Target: An Example of Controlled Active Vision

Institute for Software Researc

Using Control and Vision for Space Applications

Institute for Software Researc

Feature Based Robotic Visual Tracking of 3-D Translational Motion

Institute for Software Researc

Recognition of 2D Shapes through Contour Metamorphosis

A novel method for 2D shape recognition is proposed. The method employs as a dissimilarity measure the degree of morphing between a test shape and a reference shape. A physics-based approach substantiates the degree of morphing as a deformation energy and casts the problem as an energy minimization problem. The method operates upon key segmentation points that are provided by an appropriate segmentation algorithm. The recognition paradigm is invariant to translation, rotation, and scaling. It can handle both convex and non-convex shapes. The proposed system exhibits robust recognition behavior and real-time performance in a series of experiments. The experiments also highlight the ability of the method to recognize deformable shapes

Adaptive Control Techniques for Dynamic Visual Repositioning of Hand-Eye Robotic Systems

Using active monocular vision for 3-D visual control tasks is difficult since the translational and the rotational degrees of freedom are strongly coupled. This paper addresses several issues in 3D visual control and presents adaptive control schemes for the the problem of robotic visual servoing (eye-in-hand configuration) around a static rigid target. The objective is to move the image projections of several feature poinb of the static rigid target to some desired image positions The inverse perspective transformation is assumed partially unknown. The adaptive controllers compensate for the servoing errors, the partially unknown camera parameters, and the computational delays which are introduced by the time-consuming vision algorithms. We present a stability analysis along with a study of the conditions that the feature points must satisfy in order for the problem to be solvable. Finally, several experimental results are presented to verify the validity and the efficacy of the proposed algorithms.</p

Real-Time LQG Robotic Visual Tracking

In this paper, a modified LQG technique is proposed for the solution of the robotic visual tracking problem (eye-in-hand configuration). The problem of mbotic visual tracking is formulated as a problem of combining control with computer vision. A crosstorrelation method provides the object's motion measurements which are used to update the system's measurement vector. These measurements arc fed to a discrete steady state Kalman filter that calculates the estimated values of the system's states and of the exogenous disturbances. Then, a discrete LQG controller computes the desired motion of the robotic system. Experimental results are presented to show the effectiveness of the approach.</p