Development of a Virtual Collision Sensor for Industrial Robots

Indri, Marina; Trapani, Stefano; Lazzero, Ivan

doi:10.3390/s17051148

Cited by 26 publications

(18 citation statements)

References 20 publications

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“…For robotic navigation in unstructured environments, a method based on ToF cameras [ 10 ] was provided for 3D obstacle detection and classification. Meanwhile, in the area of industrial and daily application [ 11 ], the robot mechanism presents a promising prospect. A real-time collision detection algorithm for marine robotic manipulation was presented to be a useful pilot assisting tool for subsea intervention operations [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Wang

2018

Sensors

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Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN—i.e., confidence map—is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Wang

2018

Sensors

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“…In a future intelligent factory where the environment will be dynamic and unstructured, a robotic manipulator will work with humans efficiently and safely to complete a great variety of complex jobs and tasks collaboratively [ 1 , 2 , 3 , 4 ]. A collaborative manipulator firstly can perceive static obstacles and avoid obstacles autonomously.…”

Section: Introductionmentioning

confidence: 99%

A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm

Wei

Ren

2018

Sensors

198

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In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human–Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator’s obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots’ path planning.

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“…(10) Indri et al also designed a virtual impact sensor by sensing changes in joint currents. (11) The disadvantage of the current sensing method is that the collision detection threshold setting process is complicated. The dynamic model-based control method can be used for robot collision detection.…”

Section: Introductionmentioning

confidence: 99%

Robot Collision Detection and Distinction Based on Convolution Filtering Dynamic Model

Li¹,

Ye²,

Wu³

2019

Sensors and Materials

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With the increasing application of human-robot interaction, collision detection between robot and unknown environments, along with further distinction from the intentional contact between human and robot, have become urgent problems to be solved. In this paper, a new collision detection algorithm is proposed, and a collision distinction method is further designed on the basis of this algorithm. The generalized momentum and the convolution method are used to develop the robot convolution filtering dynamic model. Then, the force-sensing observer that uses only proprioceptive sensors is designed to observe the torque deviation of the joint online to realize robot collision detection. At the same time, the performance of the forcesensing observer is improved by compensating for joint friction. The proposed algorithm does not need any external sensors; it overcomes the disadvantage of calculation errors owing to the acquisition of joint acceleration information. The filter can be flexibly selected in the algorithm according to the actual application of the robot. Moreover, two force-sensing observers are adopted in the collision distinction method. The contact or collision between a human and a robot can be further distinguished after setting the appropriate thresholds and filtering parameters. The collision detection algorithm can be easily adapted to different types of robot to ensure human safety, and the proposed collision distinction method can be used to improve the work efficiency of the robot. External force sensing experiments show that the low-pass and bandpass observers work well and different force signals can be observed. The collision detection and human-robot interaction experiments are performed to verify that the collision detection algorithm and collision distinction method are reasonable and effective.

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Development of a Virtual Collision Sensor for Industrial Robots

Cited by 26 publications

References 20 publications

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm

Robot Collision Detection and Distinction Based on Convolution Filtering Dynamic Model

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