Versatile visual servoing without knowledge of true Jacobian

By K. Hosoda, Minoru Asada. 1994

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Citations in the corpus

ViSP for visual servoing: a generic software platform with a wide class of robot control skills (2005, background): “These possibilities have been integrated in V ISP but other possibilities (such as a learning process of the interaction matrix [18, 20, 21]) are always possible t hough they are not currently integrated in the software.”
Autonomous Online Learning of Reaching Behavior in a humanoid Robot (2012, methodology): “The open-loop controller uses a head-arm kinematic map, implemented with a Receptive Field Neural Network [3], while the closedloop controller uses a visual-arm Jacobian map, realized through the combined use of a Receptive Field Neural Network and an incremental least square algorithm [1].”
Cognitive Developmental Robotics: A Survey (2009, background): “A more adaptive approach makes a robot estimate these parameters based on its experience in the environment [84]–[86].”
Uncalibrated visual servoing of robots using a depth-independent interaction matrix (2006, methodology): “Hosoda and Asada [ 16 ] used the Broyden updating formula to estimate the image Jacobian.”
A Developmental Roadmap for Learning by Imitation in Robots (2007, methodology): “A particularly useful method for online estimation of visual motor relations is based on the Broyden update rule, well known from optimization theory [37], and used in real robotic applications with visual control [38], [39].”
Visual servoing of an under-actuated dynamic rigid-body system: an image-based approach (2002, methodology): “Various approaches to overcoming the relative depth requirement for classical IBVS have been reported, including; estimation via partial pose estimation [17], adaptive control [20], and estimation of the image Jacobian using quasi-Newton techniques [10], [21].”
Experimental evaluation of uncalibrated visual servoing for precision manipulation (1997, background): “[ 19 ]), or (partially) model based schemas (eg.[2]), as long as the estimate accuracy is similar.”
Visual servoing for humanoid grasping and manipulation tasks (2008, background): “by image features, and through the movements of these features a local Image-Jacobian (Feature-Sensitivity Matrix, Interaction Matrix, B Matrix) can be derived ([5], [6], [7],”
Survey on Visual Servoing for Manipulation (2002, background): “(Hosoda & Asada 1994) present a control scheme where the kinematic structure of the system is completely unknown and coupled robot-image Jacobian is estimated.”