A Survey and Analysis of Multi-Robot Coordination

Yan, Zhi; Jouandeau, Nicolas; Chérif, Arab Ali

doi:10.5772/57313

Cited by 515 publications

(334 citation statements)

References 160 publications

(241 reference statements)

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“…Figure 28 shows scenes of experiments. (3) shows that Roomba failed to carry the box. If Roomba had not failed, Roomba would have carried the ball.…”

Section: Verificationmentioning

confidence: 99%

“…Recent studies on decision-making of multirobot systems focus on two categories: allocation (centralized method for coordinating) and cooperation (decentralized method for coordinating). Yan et al [3] valued the following two categories: the allocation that the central controller has a global view of the world, whereby the globally optimal plans can be produced, but it is not robust in relation to dynamic environments or failures in communications and other uncertainties and the cooperation can better respond to unknown or uncertain environments, but the solutions they reach are often suboptimal. In the real world, multirobot systems will be offered various tasks of the unknown and 2 Journal of Robotics uncertain environments.…”

Section: Introductionmentioning

confidence: 99%

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Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Morisawa

Hayashi

Mizuuchi

2018

Journal of Robotics

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We present a method to allocate multiple tasks with uncertainty to heterogeneous robots using the theory of comparative advantage: an economic theory that maximizes the benefit of specialization. In real applications, robots often must execute various tasks with uncertainty and future multirobot system will have to work effectively with people as a team. As an example, it may be necessary to explore an unknown environment while executing a main task with people, such as carrying, rescue, military, or construction. The proposed task allocation method is expected to reduce the total makespan (total length of task-execution time) compared with conventional methods in robotic exploration missions. We expect that our method is also effective in terms of calculation time compared with the time-extended allocation method (based on the solution of job-shop scheduling problems). We simulated carrying tasks and exploratory tasks, which include uncertainty conditions such as unknown work environments (2 tasks and 2 robots, multiple tasks and 2 robots, 2 robots and multiple tasks, and multiple tasks and multiple robots). In addition, we compared our method with full searching and methods that maximize the sum of efficiency in these simulations by several conditions: first, 2 tasks (carrying and exploring) in the four uncertain conditions (later time, new objects appearing, disobedient robots, and shorter carrying time) and second, many types of tasks to many types of robots in the three uncertain conditions (unknown carrying time, new objects appearing, and some reasonable agents). The proposed method is also effective in three terms: the task-execution time with an increasing number of objects, uncertain increase in the number of tasks during task execution, and uncertainty agents who are disobedient to allocation orders compared to full searching and methods that maximize the sum of efficiency. Additionally, we performed two real-world experiments with uncertainty.

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“…Figure 28 shows scenes of experiments. (3) shows that Roomba failed to carry the box. If Roomba had not failed, Roomba would have carried the ball.…”

Section: Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Morisawa

Hayashi

Mizuuchi

2018

Journal of Robotics

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“…In this case, an individual robot makes independent decisions on how to behave, based on the information it gathers through its own perception with others. When the robots use an implicit communication to coordinate, although the information obtained by the robots is not completely reliable, the stability, reliability and fault tolerance of the overall system can be improved [9,10].…”

Section: Related Workmentioning

confidence: 99%

“…Constraint (10) ensures that each cell is visited at least once. Constraint (11) defines that each target z must be handled safely by N R min robots.…”

Section: Objective Function and Constraintsmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

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Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two subproblems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

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“…The use of several robots, integrated either within an environment with human operators or other robots, is likely to increase the efficiency of a process and to minimize the total operation time of a given task. In a survey on Multi-Robot coordination, Yan et al (2013) discussed motivating factors that support the use of multiple manipulator robotic systems. In that review, some of the discussed advantages associated to multi-robot systems included:…”

Section: Introductionmentioning

confidence: 99%

Multiple manipulators path planning using double A*

Tavares

Lima

Costa

et al. 2016

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Abstract. Purpose -Streamlining automated processes are currently undertaken by developing optimization methods and algorithms for robotic manipulators. In this paper a new approach to improve streamlining of automatic processes is presented. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high flexibility solution to automated processes. Design/ Methodology / Approach -The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path planning algorithm. Gazebo was the simulation engine chosen and the robotic manipulator used was the universal robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge. Findings The developed system was able to identify and describe the influence of each joint in the Cartesian space; and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene. Practical implications -This new system was tested in both real and simulated environments and data collected showed that this new system performed well in real life scenarios, such as EuRoC and Amazon Picking Challenge. Originality / Value The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulators path and motion planning.

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A Survey and Analysis of Multi-Robot Coordination

Cited by 515 publications

References 160 publications

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Multiple manipulators path planning using double A*

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