Multi-robot multi-object rearrangement in assignment space

Levihn, Martin; Igarashi, Takeo; Stilman, Mike

doi:10.1109/iros.2012.6386013

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Previous work dealt with long-horizon construction planning for a single agent [57], or two agents [8], but solving long-horizon TAMP problems using multiple robots is an open challenge. Initial steps toward solving multi-robot, multiobject rearrangement tasks in a simple configuration space for homogeneous robot teams with few objects were made in [11] and [58]. Such previous work assumes that the actions of robots are synchronized [8], [10], [11], [25], plan in the combined space of all robots, and do therefore not scale [8], are only demonstrated for simple robots and few objects and state that they do not expect to scale [58], or are not demonstrated on long time-horizons [11], [25].…”

Section: B Assembly Planningmentioning

confidence: 99%

“…We did not put an emphasis on comparison with other methods, as the other methods we are aware of ( [8], [11], [12], [25], [58], [68]) do not scale to the number of robots and objects we consider. We compare our method to a fixed-time sampling scheme, 5 which decomposes the problem as the presented approach does, and uses prioritized planning, but does not allow for variable durations of the tasks, i.e., all mode-switches take place at a multiple of T .…”

Section: Comparison To Fixed-time-samplingmentioning

confidence: 99%

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Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

et al. 2023

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Robotic construction assembly planning aims to find feasible assembly sequences as well as the corresponding robotpaths 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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Section: B Assembly Planningmentioning

confidence: 99%

Section: Comparison To Fixed-time-samplingmentioning

confidence: 99%

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

et al. 2023

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“…However, while previous work dealt with long-horizon construction planning for two agents [16], solving long-horizon TAMP problems using multiple robots as they arise in our scenarios is an open challenge. Initial steps towards solving multi-robot, multi-object rearrangement tasks in a simple configuration space for homogeneous robot teams with few objects were made in [30]. With our approach, we are able to scale to many more objects and robots, using heterogeneous robot teams with more complex interaction in complex configuration spaces.…”

Section: B Assembly Planningmentioning

confidence: 99%

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Hartmann¹,

Orthey²,

Driess³

et al. 2021

Preprint

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Robotic assembly planning has the potential to profoundly change how buildings can be designed and created. It enables architects to explicitly account for the assembly process already during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling assembly planning with large, heterogeneous teams of robots. We present a scalable planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller sub-problems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multirobot manipulation with unknown arrival-times. Thus, our solver allows for completing sub-problems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies and on two long-horizon building assembly scenarios to show the robustness and scalability of our algorithm.

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“…Similar problems arise in robotics. For example, such problems arise when a robot needs to arrange products on a shelf in a store, or when a robot needs to move objects around in order to access a specific product that needs to be picked up; see, e.g., [16][17][18]. In robotics, these problems are often referred to as object rearrangement problems.…”

Section: Introductionmentioning

confidence: 99%

Space-Aware Reconfiguration

Halperin¹,

Kreveld²,

Miglioli-Levy³

et al. 2020

Preprint

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We consider the problem of reconfiguring a set of physical objects into a desired target configuration, a typical (sub)task in robotics and automation, arising in product assembly, packaging, stocking store shelves, and more. In this paper we address a variant, which we call space-aware reconfiguration, where the goal is to minimize the physical space needed for the reconfiguration, while obeying constraints on the allowable collision-free motions of the objects. Since for given start and target configurations, reconfiguration may be impossible, we translate the entire target configuration rigidly into a location that admits a valid sequence of moves, where each object moves in turn just once, along a straight line, from its starting to its target location, so that the overall physical space required by the start, all intermediate, and target configurations for all the objects is minimized.We investigate two variants of space-aware reconfiguration for the often examined setting of n unit discs in the plane, depending on whether the discs are distinguishable (labeled) or indistinguishable (unlabeled). For the labeled case, we propose a representation of size Opn 4 q of the space of all feasible initial rigid translations, and use it to find, in Opn 6 q time, a shortest valid translation, or one that minimizes the enclosing disc or axis-aligned rectangle of both the start and target configurations. For the significantly harder unlabeled case, we show that for almost every direction, there exists a translation in this direction that makes the problem feasible. We use this to devise heuristic solutions, where we optimize the translation under stricter notions of feasibility. We present an implementation of such a heuristic, which solves unlabeled instances with hundreds of discs in seconds.

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Multi-robot multi-object rearrangement in assignment space

Cited by 7 publications

References 15 publications

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Space-Aware Reconfiguration

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