Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Robotic construction assembly planning aims to find feasible assembly sequences as well as the corresponding robot-paths and can be seen as a special case of task and motion planning (TAMP). As construction assembly can well be parallelized, it is desirable to plan for multiple robots acting concurr...

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Vydáno v:IEEE transactions on robotics Ročník 39; číslo 1; s. 239 - 252
Hlavní autoři: Hartmann, Valentin N., Orthey, Andreas, Driess, Danny, Oguz, Ozgur S., Toussaint, Marc
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Assembly
Bidirectional control
Construction planning
Feasibility
Manipulation planning
Motion planning
multi-robot systems
Multiple robots
Optimization
Planning
Robot arms
Robot kinematics
robotics and automation in construction
Robots
Service robots
Task analysis
task planning
Trajectory
ISSN:1552-3098, 1941-0468
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Shrnutí:Robotic construction assembly planning aims to find feasible assembly sequences as well as the corresponding robot-paths and can be seen as a special case of task and motion planning (TAMP). As construction assembly can well be parallelized, it is desirable to plan for multiple robots acting concurrently. Solving TAMP instances with many robots and over a long time-horizon is challenging due to coordination constraints, and the difficulty of choosing the right task assignment. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to jointly solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple construction case-studies to show the robustness over long planning horizons and scalability to many objects and agents. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2022.3198020