14 research outputs found

    Bidirection modeling and experimental analysis of underwater snake robot

    Get PDF
    Snakes have dedicate body and can maneuver in challenging environments. In this work, a soft snake-like robot is designed to locomote like a biological snake that can be used in search and rescue operation. The soft snake-like for underwater use has advantages of low inertia, high buoyancy, and more structural flexibility. Currently, the use of multi-redundant thin McKibben actuators for soft snake-like robot was not yet explored. Addressing this gap, a soft snake robot model using Finite Element (FE) will be developed. The FE model will be developed and used to investigate the snake bending motions in Matlab Simulink with Simscape Multibody Library (SML). Next, the actual fabrication of the robot will be validated with the simulated FE model using redundant mechanism of 10 McKibben actuators attached on a plastic plate. The structure of this robot uses 32 cm of a thin non-rigid plastic plate with five thin muscles at both sides of the body. Each thin muscle has 2.0 mm outer diameter with internal 1.3 mm silicone tube. The manipulator will be tested with different pressure and frequencies to perform various bending motions. Tracker application will capture every phase of the bending body and movements for analysis of the robot’s movement. It is expected that the snake-like robot can move and the errors of bending angle between simulation and experiment are less than 5%

    Bio-Inspired Robotics

    Get PDF
    Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

    Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks

    Get PDF
    Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on

    Real-time Biomechanical Modeling for Intraoperative Soft Tissue Registration

    Get PDF
    Computer assisted surgery systems intraoperatively support the surgeon by providing information on the location of hidden risk and target structures during surgery. However, soft tissue deformations make intraoperative registration (and thus intraoperative navigation) difficult. In this work, a novel, biomechanics based approach for real-time soft tissue registration from sparse intraoperative sensor data such as stereo endoscopic images is presented to overcome this problem

    SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

    Get PDF
    We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement

    Bowdoin Orient v.135, no.1-25 (2005-2006)

    Get PDF
    https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1006/thumbnail.jp
    corecore