Manipulator residual estimation and its application in collision detection

Guo, Mingming; Zhang, Hua; Feng, Chuncheng; Liu, Manlu; Huo, Jianwen

doi:10.1108/ir-01-2018-0019

Cited by 17 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also generalized momentum observer (GMO) methods that analyze the robot’s momentum change for collision detection [ 30 , 31 ]. Another method based on the momentum observer can be found in [ 32 , 33 ]. However, this is a model-based approach that is highly dependent on the quality of the dynamic robot model.…”

Section: Problem Formulationmentioning

confidence: 99%

A Simple Neural Network for Collision Detection of Collaborative Robots

Czubenko

Kowalczuk

2021

Sensors

View full text Add to dashboard Cite

Due to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents the practical aspect of collision detection with the use of a simple neural architecture. A virtual force and torque sensor, implemented as a neural network, may be useful in a team of collaborative robots. Four different approaches are compared in this article: auto-regressive (AR), recurrent neural network (RNN), convolutional long short-term memory (CNN-LSTM) and mixed convolutional LSTM network (MC-LSTM). These architectures are analyzed at different levels of input regression (motor current, position, speed, control velocity). This sensor was tested on the original CURA6 robot prototype (Cooperative Universal Robotic Assistant 6) by Intema. The test results indicate that the MC-LSTM architecture is the most effective with the regression level set at 12 samples (at 24 Hz). The mean absolute prediction error obtained by the MC-LSTM architecture was approximately 22 Nm. The conducted external test (72 different signals with collisions) shows that the presented architecture can be used as a collision detector. The MC-LSTM collision detection f1 score with the optimal threshold was 0.85. A well-developed virtual sensor based on such a network can be used to detect various types of collisions of cobot or other mobile or stationary systems operating on the basis of human-machine interaction.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

A Simple Neural Network for Collision Detection of Collaborative Robots

Czubenko

Kowalczuk

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Long et al (2023) proposed an improved adaptive super-twisting momentum observer. Guo et al (2018) has proposed a new collision detection method based on adaptive residual estimation, which improves the collision detection accuracy. Therefore, this article designs a dynamic threshold collision detection method and adds a collision response strategy to further improve security.…”

Section: Introductionmentioning

confidence: 99%

Research on dynamic parameter identification and collision detection method for cooperative robots

Sun,

Song,

Wang

et al. 2023

View full text Add to dashboard Cite

Purpose The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots. Design/methodology/approach This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans. Findings Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation. Originality/value This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.

show abstract

“…To settle this disadvantage, the band-pass filter can effectively solve the residual impact signal with the moment and motion frequency of robot structure and actuator. In the whole process, it will lead to the distortion of residual signal, and even lead to the inability to estimate the real signal of contact force, so it is necessary to separate the collision torque and dynamic effect (Sharkawy and Aspragathos, 2016; Sim et al , 2017; Yang et al , 2019; Guo et al , 2018). In Tianyu et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Collision detection and force control based on the impedance approach and dynamic modelling

Wang

Zhang

2019

View full text Add to dashboard Cite

Purpose This paper aims to address the collision problem between robot and the external environment (including human) in an unstructured situation. A new collision detection and torque optimization control method is proposed. Design/methodology/approach Firstly, when the collision appears, a second-order Taylor observer is proposed to estimate the residual value. Secondly, the band-pass filter is used to reduce the high-frequency torque modeling dynamic uncertainty. With the estimate information and the torque value, a variable impedance control approach is then synthesized to guarantee that the collision is avoided or the collision will be terminated with different contact models and positions. However, in terms of adaptive linear force error, the variation of the thickness of the boundary layer is controlled by the new proximity function. Findings Finally, the experimental results show the better performance of the proposed control method, realizing the force control during the collision process. Originality/value Origin approach and origin experiment.

show abstract

Manipulator residual estimation and its application in collision detection

Cited by 17 publications

References 16 publications

A Simple Neural Network for Collision Detection of Collaborative Robots

A Simple Neural Network for Collision Detection of Collaborative Robots

Research on dynamic parameter identification and collision detection method for cooperative robots

Collision detection and force control based on the impedance approach and dynamic modelling

Contact Info

Product

Resources

About