Multi-robot cooperative formation for overweight object transportation

Eoh, Gyuho; Jeon, Jae D.; Choi, Jin Hwan; Lee, Beom H.

doi:10.1109/sii.2011.6147538

Cited by 25 publications

(10 citation statements)

References 23 publications

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“…This problem is termed the multi-robot task allocation (MRTA) problem, involving the assignment of tasks to multiple robots to achieve a given goal [1][2][3]. It is essential to assign tasks to robots efficiently for various applications of multi-robot systems, including reconnaissance [4,5], search and rescue [6,7], and transportation [8,9].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

2021

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This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

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Section: Introductionmentioning

confidence: 99%

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

2021

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“…Multi-robot formation control is one of the most important areas of research in Multi-Robot Systems [1,2], due to general practical applications such as joint handing [3], cooperative rescue [4], group stalking, and exploration [5]. The goal of multi-robot formation control is to maintain a specified geometrical shape of a group of robots by adjusting the pose (positions and orientations) of robots [6], which generally follows the process of forming, maintaining, and switching of formation.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Robot Formation Platform based on an Indoor Global Positioning System

et al. 2019

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Aimed at the problem that experimental verifications are difficult to execute due to lacking effective experimental platforms in the research field of multi-robot formation, we design a simple multi-robot formation platform. This proposed general and low-cost multi-robot formation platform includes the indoor global-positioning system, the multi-robot communication system, and the wheeled mobile robot hardware. For each wheeled mobile robot in our platform, its real-time position information in the centimeter‑level precise is obtained by the Marvelmind Indoor Navigation System and orientation information is obtained by the six-degree-of-freedom gyroscope. The Transmission Control Protocol/Internet Protocol (TCP/IP) wireless communication infrastructure is selected to support the communication among robots and the data collection in the process of experiments. Finally, a set of leader–follower formation experiments are performed by our platform, which include three trajectory tracking experiments of different types and numbers under deterministic environment and a formation-maintaining experiment with external disturbances. The results illustrate that our multi-robot formation platform can be effectively used as a general testbed to evaluate and verify the feasibility and correctness of the theoretical methods in the multi-robot formation. What is more, the proposed simple and general formation platform is beneficial to the development of platforms in the fields of multi-robot coordination, formation control, and search and rescue missions.

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“…They have received great attention because of their capability to perform tasks which may be too difficult or time consuming for a single robot system. Examples of these applications are surveillance tasks Saska et al (2014); Grocholsky et al (2006), grasping of heavy objects Eoh et al (2011). Formation control of multi-vehicle systems is important to perform spatially distributed tasks.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Source Seeking with Leader-Follower Formation Control∗∗This work was supported by the Research Council of Norway through the Centres of Excellence funding scheme, Project numbe 223254 - AMOS.

2015

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In this paper a source seeking method for multi-agent systems consisting of autonomous surface vessels (ASVs) or autonomous underwater vehicles (AUVs) is proposed. The desired heading for the leader, and thus the desired heading for the group, is adapted based on gradient information from the field. That is, an initial given trajectory is assigned to the leader. Then using scalar measurements from each agent the approximated gradient of the field is used to compute the direction towards the source. The initial and computed heading are weighted with a parameter that depends on the intensity of the source's signal. If the signal exceeds a given threshold, the agents move towards the source. During source seeking a desired formation is achieved and maintained using leader-follower synchronization controllers. A case study is presented and discussed to illustrate the approach.

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Multi-robot cooperative formation for overweight object transportation

Cited by 25 publications

References 23 publications

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

A Multi-Robot Formation Platform based on an Indoor Global Positioning System

Adaptive Source Seeking with Leader-Follower Formation Control∗∗This work was supported by the Research Council of Norway through the Centres of Excellence funding scheme, Project numbe 223254 - AMOS.

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