To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Online and Scalable Motion Coordination for Multiple Robot Manipulators in Shared Workspaces
School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing, China.
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-9652-7864
2023 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783Article in journal (Refereed) Epub ahead of print
Abstract [en]

Multi-robot motion coordination is essential to make robots work safely and efficiently in a shared workspace, ensuring task completion while avoiding interference between each other's motions. Achieving online replanning and scaling to large numbers of robots are particularly challenging. In this paper, we present a motion coordination method for multiple robot manipulators with given targets. The approach separates the problem into a global coordination stage and a local trajectory replanning stage. Online reactivity and scalability are achieved by means of coordination in a reduced space and efficient trajectory replanning. We first introduce the method for systems with two robots, then extend it to systems with an arbitrary number of robots. The method determines whether kinematically-feasible and non-interfering trajectories leading all the robots to their targets exist. The approach generates time-efficient trajectories if solutions exist, or provides information for switching targets if solutions cannot be found. We show formally and empirically that the method has low computational overhead and scales quadratically with the number of robots. Experiments are conducted with up to three real 7-DOF robots and up to ten simulated robots. Note to Practitioners-In underground mining, a key process is that of tunneling, i.e., drilling and blasting rock to excavate tunnels that lead to sources of ore. Drilling is carried out by rigs equipped with multiple robotic arms. A key factor affecting the efficiency of tunneling is the time to completion of drilling operations. In current industrial practice, these operations are carried out manually by an operator steering the arms on the drill rig. Time to completion can be drastically reduced if the arms could operate concurrently, intelligently optimizing and coordinating their motions. However, existing methods for multi-arm motion coordination are inadequate, as they fail to cater to one or more of the following real-world constraints: several robot arms work in close proximity and their workspaces are overlapping; task completion times are uncertain due to rock density and drill bit breakage; and contingencies such as unexpected delays in motion or stops sometimes happen. These constraints exist also in other industrial applications, like manufacturing and assembly. This paper proposes a framework which enables multiple high-DOF robot manipulators to safely and efficiently work in a shared workspace. The method allows to adjust robot trajectories while robots move, and is shown to scale well with the number of robots. Motions are generated and adjusted by time-optimal trajectory planning, whose computational overhead is small enough for online operation and benefits task completion efficiency, safety, and productivity. Experiments using three 7-DOF robots suggest that this approach is practically feasible, and simulations with up to 10 robots attest to its scalability.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023.
Keywords [en]
Robots, Robot kinematics, Collision avoidance, Trajectory, Manipulators, Task analysis, Planning, Multi-robot systems, scheduling and coordination, real-time planning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-105936DOI: 10.1109/TASE.2023.3266889ISI: 000976057700001OAI: oai:DiVA.org:oru-105936DiVA, id: diva2:1756832
Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-05-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Pecora, Federico

Search in DiVA

By author/editor
Pecora, Federico
By organisation
School of Science and Technology
In the same journal
IEEE Transactions on Automation Science and Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 40 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf