A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction
Open Access
- 17 June 2021
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Robotics and AI
Abstract
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. 1This publication has 26 references indexed in Scilit:
- A computationally efficient safety assessment for collaborative robotics applicationsRobotics and Computer-Integrated Manufacturing, 2017
- A Survey of Methods for Safe Human-Robot InteractionFoundations and Trends® in Robotics, 2017
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- You Only Look Once: Unified, Real-Time Object DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Real-Time Computation of Distance to Dynamic Obstacles With Multiple Depth SensorsIEEE Robotics and Automation Letters, 2016
- Visual ServoingPublished by Springer Science and Business Media LLC ,2016
- Microsoft COCO: Common Objects in ContextLecture Notes in Computer Science, 2014
- Point-based and region-based image moments for visual servoing of planar objectsIEEE Transactions on Robotics, 2005
- ViSP for visual servoing: a generic software platform with a wide class of robot control skillsIEEE Robotics & Automation Magazine, 2005
- Real-Time Obstacle Avoidance for Manipulators and Mobile RobotsPublished by Springer Science and Business Media LLC ,1986