Synchronized Multi-arm Rearrangement Guided by Mode Graphs with Capacity Constraints

Shome, Rahul; Bekris, Kostas E.

doi:10.1007/978-3-030-66723-8_15

Cited by 19 publications

(25 citation statements)

References 30 publications

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“…On the lowest level, construction planning needs to solve a multi-robot motion planning problem for heterogeneous robot teams [23]. This problem is often divided into two categories: first, one can plan roadmaps in parallel for individual robots, combine those roadmaps into an (implicit) joint state space roadmap, and eventually search this roadmap using algorithms, such as M* [24], or discrete RRT [25], [26]. Those algorithms can often be significantly improved using heuristics learned from prior experience [27].…”

Section: A Multi-robot Motion Planningmentioning

confidence: 99%

“…Such a constraint graph can be exploited by biased sampling at constraint intersections [45], [46], and these samples can be connected along the constraint manifolds using projection methods [47], [48], [49]. Complex applications of such an approach are demonstrated in [25], such as concurrent handovers between multiple robots with multiple objects and capacity constraints.…”

Section: B Assembly Planningmentioning

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: A Multi-robot Motion Planningmentioning

confidence: 99%

Section: B Assembly Planningmentioning

confidence: 99%

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

et al. 2023

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“…Object reconfiguration has been studied in contexts including manipulation planning [4,5,41,42], rearrangement planning [1,2,43,44], and integrated task and motion planning (TAMP) [10]. These approaches span an axis ranging from problem specialization (i.e., planar rearrangement planners [1,2,43]) to relative generality (i.e., full TAMP solving [3,6,7]).…”

Section: Object Reconfigurationmentioning

confidence: 99%

Object Reconfiguration with Simulation-Derived Feasible Actions

Lee¹,

Thomason²,

Kingston³

et al. 2023

Preprint

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3D object reconfiguration encompasses common robot manipulation tasks in which a set of objects must be moved through a series of physically feasible state changes into a desired final configuration. Object reconfiguration is challenging to solve in general, as it requires efficient reasoning about environment physics that determine action validity. This information is typically manually encoded in an explicit transition system. Constructing these explicit encodings is tedious and error-prone, and is often a bottleneck for planner use. In this work, we explore embedding a physics simulator within a motion planner to implicitly discover and specify the valid actions from any state, removing the need for manual specification of action semantics. Our experiments demonstrate that the resulting simulation-based planner can effectively produce physically valid rearrangement trajectories for a range of 3D object reconfiguration problems without requiring more than an environment description and start and goal arrangements.

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“…Building on MMMP, work in Task and Motion Planning (TAMP) bridges symbolic reasoning about actions that achieve discrete goals and geometric reasoning in search of collision-free robotic motions [3]. Most existing MMMP and TAMP methods are only able to plan for sequential systems, such as a single robot [3] or a team of synchronized robots [4], and are unable to represent plans where manipulation can happen asynchronously, for example when one robot places an object while other robots move according to their current manipulation modes. Existing algorithms for multi-agent TAMP [5] are capable of modeling multi-arm assembly problems but have not demonstrated the ability to solve TAMP problems with the long horizons and close robot proximity that are required in multi-arm assembly.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

Chen¹,

Li²,

Huang³

et al. 2022

Preprint

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Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.

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Synchronized Multi-arm Rearrangement Guided by Mode Graphs with Capacity Constraints

Cited by 19 publications

References 30 publications

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Object Reconfiguration with Simulation-Derived Feasible Actions

Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

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