1,898 research outputs found

    Active Vision for Scene Understanding

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    Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot\u27s view in order to explore interaction possibilities of the scene

    Autonomous task-based grasping for mobile manipulators

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    A fully integrated grasping system for a mobile manipulator to grasp an unknown object of interest (OI) in an unknown environment is presented. The system autonomously scans its environment, models the OI, plans and executes a grasp, while taking into account base pose uncertainty and obstacles in its way to reach the object. Due to inherent line of sight limitations in sensing, a single scan of the OI often does not reveal enough information to complete grasp analysis; as a result, our system autonomously builds a model of an object via multiple scans from different locations until a grasp can be performed. A volumetric next-best-view (NBV) algorithm is used to model an arbitrary object and terminates modelling when grasp poses are discovered on a partially observed object. Two key sets of experiments are presented: i) modelling and registration error in the OI point cloud model is reduced by selecting viewpoints with more scan overlap, and ii) model construction and grasps are successfully achieved while experiencing base pose uncertainty. A generalized algorithm is presented to discover grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using sensed point clouds. The algorithm introduces two key ideas: 1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogram of object(s) surface normals, and 2) voxel grid representations of gripper and object(s) are cross-correlated to match finger contact points, i.e. grasp “size”, to discover a grasp pose. Constraints, such as collisions with neighbouring objects, are incorporated in the cross-correlation computation. Simulations and preliminary experiments show that 1) grasp poses for three grasp types are found in near real-time, 2) grasp pose solutions are consistent with respect to voxel resolution changes for both partial and complete point cloud scans, 3) a planned grasp pose is executed with a mechanical gripper, and 4) grasp overlap is presented as a feature to identify regions on a partial object model ideal for object transfer or securing an object

    Efficient Environment Guided Approach for Exploration of Complex Environments

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    Remote inspection of a complex environment is a difficult, time consuming task for human operators to perform. The need to manually avoid obstacles whilst considering other performance factors i.e. time taken, joint effort and information gained represents significant challenges to continuous operation. This paper proposes an autonomous robotic solution for exploration of an unknown, complex environment using a high DoF robot arm with an eye in hand depth sensor. The main contribution of this work is a new strategy to find the next best view by evaluating frontier regions of the map to maximise coverage, in contrast to many current approaches which densely sample joint or workspace configurations of the robot. Multiple utility functions were evaluated that showed different behaviours. Our results indicated that the presented algorithm can explore an arbitrary environment efficiently while optimising various performance criteria based on the utility function chosen, application constraints and the desires of the user

    High-precision grasping and placing for mobile robots

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    This work presents a manipulation system for multiple labware in life science laboratories using the H20 mobile robots. The H20 robot is equipped with the Kinect V2 sensor to identify and estimate the position of the required labware on the workbench. The local features recognition based on SURF algorithm is used. The recognition process is performed for the labware to be grasped and for the workbench holder. Different grippers and labware containers are designed to manipulate different weights of labware and to realize a safe transportation

    Humanoid Robots

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    For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

    Streaming Monte Carlo Pose Estimation for Autonomous Object Modeling

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    This work contributes the optimization of a streaming pose estimation particle filter and its integration into an autonomous object modeling approach. The particle filter is advanced by an additional pose optimization in the particle weighting step. By integrating the method into the autonomous object modeling approach, the repositioning of objects is enabled, which is often necessary in order to acquire complete models. Experiments show that the usage of iterative closest point is too restrictive for general transformations. The used Monte Carlo method enables a robust pose estimation without loss of time and with high precision. Further, it is shown that the overall modeling results are improved clearly

    Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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    Active Vision for Scene Understanding

    Get PDF
    Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene
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