Discusses the design and modeling fundamentals of a multiple degree of freedom reconfigurable robotic gripper system (RGS), designed to reliably automate the process of limp material handling without causing distortio...
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Discusses the design and modeling fundamentals of a multiple degree of freedom reconfigurable robotic gripper system (RGS), designed to reliably automate the process of limp material handling without causing distortion, deformation and/or folding. The reconfigurable gripper design draws upon the authors' (2000) previously reported work on the flat surfaced, fixed-dimensions gripper system. The design of the reconfigurable gripper system consists of four arms in a cross-bar configuration, with a flat surfaced, fixed dimensions, suction based gripper unit mounted on each of the arms. In the paper, the kinematic and dynamic performances of the reconfigurable robotic gripper system are analyzed and then validated using 1-DEAS software.
Ultrasonic range sensors are used to obtain the information required for collision-free navigation of a mobile robot in a semi-structured or unstructured environment. A set of range readings from a ring of sonars are ...
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This paper proposes a solution to the path planning problem for an autonomous vehicle utilizing a true electrostatic potential field developed through a resistor network which is representative of the environment. The...
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This paper proposes a solution to the path planning problem for an autonomous vehicle utilizing a true electrostatic potential field developed through a resistor network which is representative of the environment. The function which generates the potential field is taken from a naturally occurring potential field, as opposed to an artificially created potential function. The result of the natural potential function is a field which has local minima only at locations chosen by the implementation (places of applied current source) and generates paths in a real time frame, starting at the initial position of the vehicle and terminating at the goal position. Simulation and real-time experiments were carried out to test the developed implementation.
Autonomous underwater vehicles (AUVs) share common control problems with other air, land, and water unmanned vehicles. In addition to requiring high-dimensional and computationally intensive sensory data for real-time...
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Autonomous underwater vehicles (AUVs) share common control problems with other air, land, and water unmanned vehicles. In addition to requiring high-dimensional and computationally intensive sensory data for real-time mission execution, power and communication limitations in an underwater environment make it more difficult to develop a control architecture for an AUV. In this article, the four types of control architectures being used for AUVs (hierarchical, heterarchical, subsumption, and hybrid architecture) are reviewed. A summary of 25 existing AUVs and a review of 11 AUV control architecture systems present a flavor of the state of the art in AUV technology. A new sensor-based embedded AUV control system architecture is also described and its implementation is discussed.
The construction and analysis of multiple-input multiple-output (MIMO) subnets is discussed. It is shown that hierarchical time-extended Petri nets (H-EPNs) allow the development of structured MIMO subnets through the...
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The construction and analysis of multiple-input multiple-output (MIMO) subnets is discussed. It is shown that hierarchical time-extended Petri nets (H-EPNs) allow the development of structured MIMO subnets through the use of bottom-up Petri net synthesis techniques. The paper analyzes Petri net extensions that allow a truly hybrid approach to Petri net based systems modeling, analysis and development. The case study emphasizes the advantage of the activator arc extension in studying issues such as static priority scheduling, dynamic failure recognition, and rescheduling in manufacturing systems. The SPNP package, suitably modified to handle the H-EPN extensions, is used to analyze the properties of the derived H-EPN model.
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