Abstract
The new paradigms of Industry 4.0 demand the collaboration between robot and humans. They could help and collaborate each other without any additional safety unlike other manipulators. The robot should have the ability of acquire the environment and plan (or re-plan) on-the-fly the movement avoiding the obstacles and people. This paper proposes a system that acquires the environment space, based on a kinect sensor, performs the path planning of a UR5 manipulator for pick and place tasks while avoiding the objects, based on the point cloud from kinect. Results allow to validate the proposed system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barraquand, J., Latombe, J.C.: Robot motion planning. a distributed representation approach. Int. J. Robot. Res. 10(6), 628–649 (1991)
Ralli, E., Hirzinger, G.: Fast path planning for robot manipulators using numerical potential fields in the configuration space, vol. 3, pp. 1922–1929 (1994)
Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic roadmaps for path planning in high dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996), ISSN: 1042-296X
Siméon, T., Laumond, J.P., Nissoux, C.: Visibility based probabilistic roadmaps for motion planning. Adv. Robot. 14(6), 477–494 (2000)
Wilmarth, S., Amato, N., Stiller, P.: Maprm: a probabilistic roadmap planner with sampling on the medial axis of the free space. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1024–1031 (1999)
Bohlin, R., Kavraki, L.E.: Path planning using lazy PRM. In: Proceedings of the IEEE International Conference on Robotics and Automation, San Fransisco, vol. 1, pp. 521–528 (2000)
Plaku, E., Bekris, K.E., Chen, B.Y., Ladd, A.M., Kavraki, L.E.: Sampling based roadmap of trees for parallel motion planning. IEEE Trans. Rob. 21(4), 597–608 (2005)
Helguera, C., Zeghloul, S.: A local-based method for manipulators path planning in heavy cluttered environments. In: Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, pp. 3467–3472 (2000)
Blackmore, L., Williams, B.: Optimal manipulator path planning with obstacles using disjunctive programming. In: American Control Conference, Minneapolis (2006)
Lahouar, S., Zeghloul, S., Romdhane, L.: Real-time path planning for multi-DoF manipulators in dynamic environment. Int. J. Adv. Robot. Syst. 3(2) (2006)
Tavares, P., Lima, J., Costa, P., Moreira, A.P.: Multiple manipulators path planning using double A*. Ind. Robot: Int. J. 43(6), 657–664 (2016). https://doi.org/10.1108/IR-01-2016-0006
Staranowicz, A., Brown, G.R., Morbidi, F., Mariottini, G.L.: Easy-to-use and accurate calibration of RGB-D cameras from spheres. In: Klette, R., Rivera, M., Satoh, S. (eds.) Image and Video Technology, PSIVT 2013. LNCS, vol. 8333. Springer, Heidelberg (2014)
Chitta, S.: MoveIt!: an introduction. In: Koubaa, A. (ed.) Robot Operating System (ROS). SCI, vol. 625. Springer, Cham (2016)
Walid Darwish, W., Tang, S., Wenbin, L., Chen, W.: A new calibration method for commercial RGB-D sensors. Sensors 17, 1204 (2017). https://doi.org/10.3390/s17061204
Basso, F., Pretto, A., Menegatti, E.: Unsupervised intrinsic and extrinsic calibration of a camera-depth sensor couple. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2014)
Martinez, A., Fernández, E.: Learning ROS for Robotics Programming. Packt Publishing, Birmingham (2013)
Joseph, L.: Mastering ROS for Robotics Programming. Packt Publishing, Birmingham (2015)
The Robotic Operation System Wiki, http://wiki.ros.org/ROS/Tutorials/UnderstandingTopics
ROS Industrial Training Exercises with version Kinetic, https://github.com/ros-industrial/industrial_training/wiki
Sensor supported by ROS, http://wiki.ros.org/Sensors
Sucan, I.A., Chitta, S.: MoveIt, http://moveit.ros.org
Sucan, I.A., Chitta, S.: MoveIt, http://picknik.io/moveit_wiki/index.php?title=High-level_Overview_Diagram
Sucan, I.A., Moll, M., Kavraki, L.E.: The Open Motion Planning Library. IEEE Robot. Autom. Mag. (2012), http://ompl.kavrakilab.org
Acknowledgment
Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
This work is also financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundaçao para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Brito, T., Lima, J., Costa, P., Piardi, L. (2018). Dynamic Collision Avoidance System for a Manipulator Based on RGB-D Data. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-70836-2_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70835-5
Online ISBN: 978-3-319-70836-2
eBook Packages: EngineeringEngineering (R0)