The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
V-REP: A versatile and scalable robot simulation framework
Abstract
From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.
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Citations
Cites
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Koenig, N., & Howard, A. (2005) · 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) · doi:10.1109/iros.2004.1389727 · Cited by 3,638
RRT-connect: An efficient approach to single-query path planning
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OBBTree: A Hierarchical Structure for Rapid Interference Detection
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OpenHRP: Open Architecture Humanoid Robotics Platform
Hirukawa, H., Kanehiro, F., & Kajita, S. (2007) · Springer Tracts in Advanced Robotics · doi:10.1007/3-540-36460-9_7 · Cited by 246
OpenHRP: Open Architecture Humanoid Robotics Platform
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WebotsTM: Professional Mobile Robot SimulationPreprint
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Cited by
Tai, L., Paolo, G., & Liu, M. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202134 · Cited by 834
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning
Zeng, A., Song, S., Welker, S., Lee, J., Rodriguez, A., & Funkhouser, T. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593986 · Cited by 606
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Makoviychuk, V., Wawrzyniak, L., Guo, Y., Lu, M., Storey, K., Macklin, M., Hoeller, D., Rudin, N., Allshire, A., Handa, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.10470 · Cited by 509
Synthetic Data for Deep Learning
Nikolenko, S. I. (2021) · Springer Optimization and Its Applications · doi:10.1007/978-3-030-75178-4 · Cited by 423
A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418
Metadata
- Authors
- Rohmer, E., Singh, S. P. N., & Freese, M.
- Venue
- 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2013-11
- Pages
- 1321-1326
- OpenAlex
- W2082511574
- Cited by
- 1,446
- References
- 10
- FWCI
- 34.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rohmer, E., Singh, S. P. N., & Freese, M. (2013). V-REP: A versatile and scalable robot simulation framework. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2013.6696520
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