A depth space approach to human-robot collision avoidance

By F. Flacco, Torsten Kröger, Alessandro De Luca, Oussama Khatib. IEEE International Conference on Robotics and Automation, 2012

429 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f35b1aee6fc5c9c1784c722f88f2a0b552ed9d2c

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

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Citations in the corpus

Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments (2017, methodology): “For example, in [104] the depth sensor of the”
Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (2019, background): “In [119], the authors presented a real-time collision avoidance approach based on depth sensor for safe HRI.”
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios (2020, background): “The typical pipeline is first predicting pedestrian movements (Alahi et al., 2016; Gupta et al., 2018; Kim et al., 2015; Yi et al., 2016) and then using reactive control (similar to Flacco et al. (2012)) or local planning for collision avoidance.”
Symbiotic human-robot collaborative assembly (2019, background): “More recently, an integrated approach for collision avoidance using the depth information from Kinect sensors was reported [59,60,218].”
Deep Visual MPC-Policy Learning for Navigation (2019, methodology): “Traditionally, both navigation and obstacle avoidance have been performed using signals from Lidar or depth sensors [1, 2].”
GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation (2018, methodology): “Depth measurements can be obtained from 2D/3D lidars [10], [11], [12], depth cameras [13], [14], stereo camera pairs [15], or single cameras with 3D reconstruction methods [16], [17], [18].”
A Survey of Methods for Safe Human-Robot Interaction (2017, background): “In contrast, Flacco et al. (2012) utilized depth sensors to estimate the distance between the robot and both static and moving obstacles.”
Robot Collisions: A Survey on Detection, Isolation, and Identification (2017, background): “Anticipating a collision is typically based on the use of additional external sensors, such as onboard vision [12], [13] or RGB-D cameras placed in the environment [14].”