Research On Mechanisms Inherent In Biological Vision Guided Motion And Robotic Hand-Eye Coordination

The key difficulty of vision based robot control consists in the nonlinear relationbetween vision space and robotic motion space. Nowadays vision based robot controlsystem designed by humanbeings is not as good as itself in the aspect of precision,stability or robustness. The control mechanisms inherent in humanbeings (Primates)decoupling the nonlinear relation between vision space and motion space in highperformance with good robustness. And so, many reaseachers did the pioneer researchesand proposed kinds of robotic hand-eye coordination sytem which inspired biologically.Firstly, the accomplishment about the research of vision guided motion control inthe domain of neurobiology science and human body movement science is analyzed,mechanisms suitable for robotic control are abstracted from which. A framework ofbiologically inspired robotic hand-eye coordination is proposed based on thesemechanisms, the framework having considered both of invariant feature and variablefeature inherent in biological internal model. And the internal model learning basedvisual feedforward control and binocular disparity based visual feedback control isintegrated in the framework.Aimed to the problem of robotic body realtime tracking and percepting, animproved CONDENSATION tracking algorithm based on geometric shape template isproposed. As for external objects, the shape of objects is discribed by B-spline mothed,and relevant improved CONDENSATION tracking algorithm is proposed. By these twodifferent recognition and tracking motheds, the robot is endowed with the ability ofself-body real time perception and the ability of differentiation self-body to externalenvironment.A learning algorithm based on BP neural network and reinforcement learning isproposed by mimicing the biological visual feed-forward control which realized byvisual-motor mapping internal model learning. The algorithm is applied in roboticvisual-motor model learning, input of learning include robotic status deviated angle andimage feature in vision, output of learning is velocity of robotic joint, the velocity can beexplained to control command to realized the feed-forward control. The results of simulation experiment showed the algorithm based on BP neural network andreinforcement learning is effective to approximate the visual-motor mapping.As traditional binocular disparity to zero control is limited by degree of freedom andpriori-knowledge, a new visual feedback control based on roundly contour feature andbinocular disparity is proposed. In this mothed, the error function is consisted bybinocular error of vision and reaching angle error, a new expanded binocular disparityzero control based visual feedback control is deduced by the error function. Theexpanded visual feedback control can resolved the problem of DOF limitation whichconsisted in traditional binocular disparity control. Furthermore, experiment of objecttracking and reaching showed the expanded visual feedback control can control thebinocular disparity to zero stably, alignmenting and positioning accurately.Finally, the hand-eye coordination robot is applied to teleoperation. As a result, asupervisory control teleoperation system and a dynamic autonomy teleoperation systemwith adjustable level of autonomy were constructed by using the autonomous hand-eyecoordination robot as telerobot. For interact with autonomous hand-eye coordinationtelerobot, a new interaction composed by natural image interface and tele-autonomyprogram module is created. 'Human operator-computer-autonomous robot' is connectedby the interaction, natural image interface provide a channel for object difinition andsetting, tele-autonomy program module provide tool for 'computer-autonomous robot'interaction. Experiments showed the teleoperation system based on autonomoushand-eye coordination robot not only exert human operator's experience to treat complexwork environment, but also exert the robot's capability of autonomy.