Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning

Brown, Kyle; Peltzer, Oriana; Sehr, Martin A.; Schwager, Mac; Kochenderfer, Mykel J.

doi:10.1109/icra40945.2020.9197527

“…To make the comparison clear and fair, we did not include the time spent for this pre-process in TABLE I. We also implement an abstraction-based method based on [29], [30], which uses a MILP-based approach for optimal task assignment and ordering, and leverages the priority-based search to plan collision-free trajectories to achieve all the assigned tasks. It does not support general STL specifications but supports the tasks in wall-1 and wall-2.…”

Section: B Comparison With Other Methodsmentioning

confidence: 99%

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Sun

¹

,

Chen

²

,

Mitra

³

et al. 2022

IEEE Robot. Autom. Lett.

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We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on timed waypoints to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around the reference path defined by those waypoints. Then the search for waypoints satisfying the STL specifications can be inductively encoded as a mixedinteger linear program. The agents following the synthesized timed waypoints have their tasks automatically allocated, and are guaranteed to satisfy the STL specifications while avoiding collisions. We evaluate the algorithm on a wide variety of benchmarks. Results show that it supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods. The implementation is available at https://github.com/sundw2014/STLPlanning.

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“…To make the comparison clear and fair, we did not include the time spent for this pre-process in TABLE I. We also implement an abstraction-based method based on [28], [29], which uses a MILP-based approach for optimal task assignment and ordering, and leverages the priority-based search to plan collision-free trajectories to achieve all the assigned tasks. It does not support general STL specifications but supports the tasks in wall-1 and wall-2.…”

Section: B Comparison With Other Methodsmentioning

confidence: 99%

Multi-agent Motion Planning from Signal Temporal Logic Specifications

Sun¹,

Chen²,

Mitra³

et al. 2022

Preprint

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We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on timed waypoints to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around the reference path defined by those waypoints. Then the search for waypoints satisfying the STL specifications can be inductively encoded as a mixedinteger linear program. The agents following the synthesized timed waypoints have their tasks automatically allocated, and are guaranteed to satisfy the STL specifications while avoiding collisions. We evaluate the algorithm on a wide variety of benchmarks. Results show that it supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods. The implementation is available at https://github.com/sundw2014/STLPlanning.

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“…A recurring theme in Task Allocation and MAPF (TA-MAPF) consists of solving a relaxed task assignment problem, for example without collisions, and then finding feasible solution paths for agents given a particular assignment. The work by [5] too, which is closest to ours, at the top level solves a task assignment problem without considering collisions between agents. For finding solutions to the PC-MAPF problem formed after task assignment, it proposes H-CBS.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we briefly explain Hierarchical-CBS (H-CBS) [5], a recently proposed algorithm that solves the PC-MAPF problem and demonstrate an example case where H-CBS fails to find an optimal path. In its pre-processing step, H-CBS finds the time required for completion of each of the tasks without taking into account collisions between agents.…”

Section: Hierarchical-cbsmentioning

confidence: 99%

“…Due to the additional cross-schedule dependencies, an optimal algorithm for the PC-MAPF problem domain will also have to consider the valid intervals for starting each planning task. The algorithm proposed by [5] is closest to our work and attempts to solve the PC-MAPF problem optimally using a hierarchical algorithm based upon CBS. We shall refer to this algorithm as Hierarchical-CBS (H-CBS).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Multi-Agent Path Finding for Precedence Constrained Planning Tasks

Kedia¹,

Jenamani²,

Hazra³

et al. 2022

Preprint

0

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Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents from their start locations to end locations. We consider an extension to this problem, Precedence Constrained Multi-Agent Path Finding (PC-MAPF), wherein agents are assigned a sequence of planning tasks that contain precedence constraints between them. PC-MAPF has various applications, for example in multi-agent pickup and delivery problems where some objects might require multiple agents to collaboratively pickup and move them in unison. Precedence constraints also arise in warehouse assembly problems where before a manufacturing task can begin, its input resources must be manufactured and delivered. We propose a novel algorithm, Precedence Constrained Conflict Based Search (PC-CBS), which finds makespan-optimal solutions for this class of problems. PC-CBS utilizes a Precedence-Constrained Task-Graph to define valid intervals for each planning task and updates them when precedence conflicts are encountered. We benchmark the performance of this algorithm over various warehouse assembly, and multi-agent pickup and delivery tasks, and use it to evaluate the sub-optimality of a recently proposed efficient baseline.

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Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning

Cited by 26 publications

References 11 publications

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Multi-agent Motion Planning from Signal Temporal Logic Specifications

Optimal Multi-Agent Path Finding for Precedence Constrained Planning Tasks

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