Details
| Original language | English |
|---|---|
| Pages (from-to) | 864-885 |
| Number of pages | 22 |
| Journal | IEEE transactions on robotics |
| Volume | 40 |
| Publication status | Published - 12 Dec 2023 |
Abstract
Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans. This work addresses the issues with vector-field-based motion generation strategies, which are often prone to local-minima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introduced cooperative dual task space and flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot. Our rigorous simulation studies and real-world experiments reinforce the claim that the framework is able to run robustly within the inner loop of modern collaborative robots with vision feedback.
Keywords
- Collision avoidance, dual-arm manipulation, motion and path planning, reactive and sensor-based planning
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
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In: IEEE transactions on robotics, Vol. 40, 12.12.2023, p. 864-885.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predictive Multi-Agent-Based Planning and Landing Controller for Reactive Dual-Arm Manipulation
AU - Laha, Riddhiman
AU - Becker, Marvin
AU - Vorndamme, Jonathan
AU - Vrabel, Juraj
AU - Figueredo, Luis F.C.
AU - Muller, Matthias A.
AU - Haddadin, Sami
PY - 2023/12/12
Y1 - 2023/12/12
N2 - Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans. This work addresses the issues with vector-field-based motion generation strategies, which are often prone to local-minima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introduced cooperative dual task space and flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot. Our rigorous simulation studies and real-world experiments reinforce the claim that the framework is able to run robustly within the inner loop of modern collaborative robots with vision feedback.
AB - Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans. This work addresses the issues with vector-field-based motion generation strategies, which are often prone to local-minima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introduced cooperative dual task space and flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot. Our rigorous simulation studies and real-world experiments reinforce the claim that the framework is able to run robustly within the inner loop of modern collaborative robots with vision feedback.
KW - Collision avoidance
KW - dual-arm manipulation
KW - motion and path planning
KW - reactive and sensor-based planning
UR - http://www.scopus.com/inward/record.url?scp=85180338259&partnerID=8YFLogxK
U2 - 10.1109/TRO.2023.3341689
DO - 10.1109/TRO.2023.3341689
M3 - Article
AN - SCOPUS:85180338259
VL - 40
SP - 864
EP - 885
JO - IEEE transactions on robotics
JF - IEEE transactions on robotics
SN - 1552-3098
ER -