A Distributed Version of the Hungarian Method for Multirobot Assignment

Chopra, Smriti; Notarstefano, Giuseppe; Rice, Matthew; Egerstedt, Magnus

doi:10.1109/tro.2017.2693377

Cited by 125 publications

(46 citation statements)

References 38 publications

(46 reference statements)

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“…Therefore, it is necessary to look for a method that allows making an assignation between two different categories, minimizing a common factor. To deal with this problem, the Hungarian method [39,40] is used to minimize allocation problems. Then, for the unlabeled case, an intermediate phase is introduced to carry out the assignment by implementing the Hungarian method for minimizing the total distance traveled by the MRS.…”

Section: Generation Of Paths For the Unlabeled Casementioning

confidence: 99%

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

Madridano

Al-Kaff

Martín

et al. 2020

Sensors

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The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.

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Section: Generation Of Paths For the Unlabeled Casementioning

confidence: 99%

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

Madridano

Al-Kaff

Martín

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Reference [27] also addresses assignments to locations, but where the robots are heterogenous in that final destinations are valued differently for each robot. Reference [28] considers a variant that includes constraints on the time of arrival. We note that there is a connection between coverage problems (e.g., [3]) and task assignment.…”

Section: Related Literaturementioning

confidence: 99%

Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Jaleel

Shamma

2020

Proc. IEEE

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Recent advances in sensing, communication, and computing technologies have enabled the use of multi-robot systems for practical applications like surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time implementable, require local information only, and guarantee some desired global performance. Distributed optimization provides a framework for designing such local decision making rules for multi-robot systems. In this tutorial, we present a collection of selected results for distributed optimization for robot networks. We will focus on two special classes of problems: (i) real-time path planning for multi-robot systems and (ii) self-organization in multi-robot systems using game-theoretic approaches. For multirobot path planning, we will present some recent approaches that are based on approximately solving distributed optimization problems over continuous and discrete domains of actions. The main idea underlying these approaches is that a variety of path planning problems can be formulated as convex optimization and submodular minimization problems over continuous and discrete action spaces, respectively. To generate local update rules that are efficiently implementable in real time, these approaches rely on approximate solutions to the global problems that can still guarantee some level of desired global performance. For game theoretic self-organization, we will present a sampling of results for area coverage and real-time target assignment. In these results, the problems are formulated as games and online updating rules are designed to enable teams of robots to achieve the collective objective in a distributed manner.

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“…Chopra et al in [76] propose a novel distributed version of the Hungarian method for solving the LAP that does not use any coordinator or shared memory. Specifically, each agent runs a local routine to execute ad hoc substeps of the centralized Hungarian method and exchanges estimates of the solution with neighboring robots.…”

Section: Distributed Coordination Approachesmentioning

confidence: 99%

Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation

et al. 2020

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One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, Σk-assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest.

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A Distributed Version of the Hungarian Method for Multirobot Assignment

Cited by 125 publications

References 38 publications

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation

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