Intelligent Robotic Systems: Design, Planning, and Control
Here is a comprehensive presentation of methodology for the design and synthesis of an intelligent complex robotic system, connecting formal tools from discrete system theory, artificial intelligence, neural network, and fuzzy logic. The necessary methods for solving real time action planning, coordination and control problems are described. A notable chapter presents a new approach to intelligent robotic agent control acting in a realworld environment based on a lifelong learning approach combining cognitive and reactive capabilities. Another key feature is the homogeneous description of all solutions and methods based on system theory formalism.
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algorithm autonomous robotic backpropagation buffer calculated CARC Cartesian collision-free components computation conceptual conﬁguration constraints constructor Data members deﬁned denotes destructor device DEVS discrete discrete event simulator end effector environment Equation equipment event executed ﬁrst geometrical model grasping forces grip points gripper ICARS implementation input conveyer intelligent reactive intelligent robotic cell inverse dynamics inverse kinematics Jacak Jacobian matrix joint space Kinematic Model linguistic variables logical name machine Machine learning manipulator manufacturing Member functions membership function methods module motion planning MOVEMENT MOVEMENT MOVEMENT neural network neurons node objects belonging observation vectors obtained off-line ofthe on-line one-step trajectory planner operation optimal output conveyer parameters path planner perfonned performed position primary fuzzy sets production route Retums the pointer robot actions robot kinematics robotic agent robotic system Rozenblit sensor sequence shown in Figure simulation speciﬁcation static strategy structure Taskplan values virtual virtual function workcell workstation