“…The robot was controlled in several modes: teleoperated from the ISS by the astronauts using a TV monitor (i.e., E1), teleoperated from the ground (i.e., E1), or follow preprogrammed control commands given by the ground (i.e., E2). The highlight of the control was a multi-local sensory feedback allowing higher LoA for the robot, as well as the use of predictive "displays" or simulations to cope with the transmission delays of up to 7 s when using the teleoperation from the ground [84]. In a subsequent mission called ROKVISS [85], a two-joint robotic manipulator was installed outside the ISS in January 2005, which could be teleoperated from the ground via a direct communication link.…”
“…The robot was controlled in several modes: teleoperated from the ISS by the astronauts using a TV monitor (i.e., E1), teleoperated from the ground (i.e., E1), or follow preprogrammed control commands given by the ground (i.e., E2). The highlight of the control was a multi-local sensory feedback allowing higher LoA for the robot, as well as the use of predictive "displays" or simulations to cope with the transmission delays of up to 7 s when using the teleoperation from the ground [84]. In a subsequent mission called ROKVISS [85], a two-joint robotic manipulator was installed outside the ISS in January 2005, which could be teleoperated from the ground via a direct communication link.…”
“…The algorithm is currently in the testing stages with real data from a multi-sensor gripper housed in the arm of a small robot (see figure 1). The key feature of this small service robot is the recently developed multi-sensor gripper with a highly integrated, miniaturized sensor technology including stiff and compliant six-axis force-torque sensing, nine laser range finders (one of them realized as a rotating scanner), tactile arrays, grasp-force control, and a stereo camera pair [11]. The data for our motion-estimation algorithm will be acquired from the nine laser range finders and the stereo camera pair.…”
In this paper, we present a system for the estimation of the surface structure and the motion parameters of a free-flying object in a tele-robotics experiment. The system consists of two main components: (i) a vision-based invariant-surface and motion estimator and (ii) a Kalman filter state estimator. We present a new algorithm for motion estimation from sparse multi-sensor range data. The motion estimates from the vision-based estimator are input to a Kalman filter state estimator for continuously tracking a free-flying object in space under zero-gravity conditions. The predicted position and orientation parameters are then fed back to the vision module of the system and serve as an initial guess in the search for optimal motion parameters. The task of the vision module is two-fold: (i) estimating a piecewise-smooth surface from a single frame of multi-sensor data and (ii) determining the most likely (in the Bayesian sense) object motion that makes data in subsequent time frames to have been sampled from the same piecewise-smooth surface. With each incoming data frame, the piecewise-smooth surface is incrementally refined. The problem is formulated as an energy minimization and solved numerically resulting in a surface estimate invariant to 3D rigid motion and the vector of motion parameters. Performance of the system is depicted on simulated and real range data.
“…(Sayers and Paul, 1994) and (Jun et al, 2009), • control of robotic systems in space, e.g. (Hirzinger et al, 1989), (Bejczy, 1994), (Wright et al, 2005), and , • micro manipulation, e.g. (Tanikawa and Arai, 1999) and (Szemes et al, 2001),…”
Section: Telemanipulation Systemsmentioning
confidence: 99%
“…Hirzinger et al (1989), Prokopiou et al (1999), and Pan et al (2006). A scheme that tries to achieves delay free position and force tracking is proposed by Shahdi and Sirouspour (2009a,b,c), where the overall predictive bilateral controller is designed using H ∞ -theory.…”
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