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Computer Science > Robotics

arXiv:2003.08517 (cs)
[Submitted on 19 Mar 2020 (v1), last revised 18 Jun 2020 (this version, v2)]

Title:Provably Constant-time Planning and Replanning for Real-time Grasping Objects off a Conveyor Belt

Authors:Fahad Islam, Oren Salzman, Aditya Agarwal, Maxim Likhachev
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Abstract:In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose an approach that meets these requirements by providing provable constant-time planning and replanning guarantees. We present it, give its analytical properties and show experimental analysis in simulation and on a real robot.
Subjects: Robotics (cs.RO)
Cite as: arXiv:2003.08517 [cs.RO]
  (or arXiv:2003.08517v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2003.08517

Submission history

From: Fahad Islam [view email]
[v1] Thu, 19 Mar 2020 00:22:33 UTC (8,005 KB)
[v2] Thu, 18 Jun 2020 21:55:19 UTC (3,505 KB)
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