Abstract
Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithms.
Highlights
In recent years, the fields of robotic application have increased rapidly
Sukmanee presents a method to distinguish between a manipulator and its surroundings using a depth sensor in [20], where the iterative least square (ILS) and iterative closest point (ICP) algorithms are used for coordinate calibration and the matching between the manipulators model and point cloud
Instead of using the priori 3D model, we have designed a selfidentification method based on the 3D point cloud to automatically generate a simplified 3D model of the robot, which is suitable for the obstacle detection
Summary
The fields of robotic application have increased rapidly. Different from traditional working environment, such as the assembly line, robots are operating in more complex and dynamic environments for new tasks, such as collaborating with humans. The eye-to-hand arrangement can provide a large enough viewing angle, but an unavoidable problem is to segment the robot itself from the environment during the point cloud processing. In [22], a novel shapesensitive point sampling approach is presented to reconstruct a low-resolution point-cloud model, which can modify sampling regions adaptively These methods can generate 3D models for a wide variety of objects, but are designed only for off-line applications. A self-identification method is proposed to automatically generate a simplified 3D model for the robot based on the point cloud in real time. The collision prediction method is designed based on the robot model and the obstacle points to achieve the real time collision avoidance feature. (iv) A collision prediction method is proposed based on the simplified 3D robot model and the obstacle points.
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