Visual servoing with safe interaction using image moments

Sadeghian, Hamid; Villani, Luigi; Kamranian, Zahra; Karami, Abbas

doi:10.1109/iros.2015.7354153

Cited by 9 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The kinetostatic safety field considers both the relative position and the relative velocity between an obstacle and points on a robot. In a study by Sadeghian et al (2015), the authors use a distance-based repulsive action during image moment-based IBVS and keep the object at the center of the image.…”

Section: Related Workmentioning

confidence: 99%

“…Considering safety when using IBVS to grasp objects, if a human hand is moving into a critical distance to the robot's tool center point (TCP) during the process that IBVS is driving the robot to the desired pose for grasping, a repulsive action can be adopted to avoid the collision with the human (Sadeghian et al, 2015). One of the possible repulsive actions is moving the robot TCP to a repulsive pose in the opposite direction of the obstacle.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction

2021

View full text Add to dashboard Cite

Safety is an important issue in human–robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision. Common repulsive actions include distance methods, potential field methods, and safety field methods. Approaches based on machine learning are less explored regarding the selection of the repulsive action. Few research works focus on the uncertainty of the data-based approaches and consider the efficiency of the executing task during collision avoidance. In this study, we describe a system that can avoid collision with human hands while the robot is executing an image-based visual servoing (IBVS) task. We use Monte Carlo dropout (MC dropout) to transform a deep neural network (DNN) to a Bayesian DNN, and learn the repulsive position for hand avoidance. The Bayesian DNN allows IBVS to converge faster than the opposite repulsive pose. Furthermore, it allows the robot to avoid undesired poses that the DNN cannot avoid. The experimental results show that Bayesian DNN has adequate accuracy and can generalize well on unseen data. The predictive interval coverage probability (PICP) of the predictions along x, y, and z directions are 0.84, 0.94, and 0.95, respectively. In the space which is unseen in the training data, the Bayesian DNN is also more robust than a DNN. We further implement the system on a UR10 robot, and test the robustness of the Bayesian DNN and the IBVS convergence speed. Results show that the Bayesian DNN can avoid the poses out of the reach range of the robot and it lets the IBVS task converge faster than the opposite repulsive pose.1

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction

2021

View full text Add to dashboard Cite

show abstract

“…which is positive semi-definite within ⌃ 1 . The time derivative of (46) using (34) is simplified as…”

Section: Proof Of Propositionmentioning

confidence: 99%

“…Velocity based task sequencing control with strict task priority is widely employed in di↵erent cases such as controlling the position of multiple points in [9] and visual servoing in [34,23]. Task prioritization in acceleration level is more complex but improves the accuracy of the task execution.…”

Section: Introductionmentioning

confidence: 99%