Real-time distributed non-myopic task selection for heterogeneous robotic teams

Smith, Andrew; Best, Graeme; Yu, Javier; Hollinger, Geoffrey A.

doi:10.1007/s10514-018-9811-9

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…In both cases, approaches often do not consider trajectory costs due to kinematic or environmental constraints. Monte Carlo tree search (MCTS) [20] methods can potentially overcome these limitations, enabling nonmyopic planning with respect to complex perception objectives and motion constraints, while providing convergence guarantees [21], [22], [23], [24], [4].…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Sukkar

Best

Yoo

2019

2019 International Conference on Robotics and Automation (ICRA)

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Section: Related Workmentioning

confidence: 99%

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Sukkar

Best

Yoo

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Omidshafiei et al [7] and Chopra et al [9] have proposed allocation methods that, however, require offline preprocessing of the map with strict spatio-temporal constraints for exploration. Best et al [8] and Smith et al [10] made use of Monte-Carlo Tree Search to incrementally produce exploration tree to share among the agents. The later, however, demands much more computation and exploration time.…”

Section: B Multi-agent Explorationmentioning

confidence: 99%

“…More recent methods employ classical search and planning algorithms to handle the exploration and search allocation tasks in a decentralized manner [7,8,9,10]. However, the distributed versions of the allocation methods in [7,9] still need some offline mapping and evaluation of the environment beforehand.…”

Section: Introductionmentioning

confidence: 99%

“…However, the distributed versions of the allocation methods in [7,9] still need some offline mapping and evaluation of the environment beforehand. On the other hand, Best et al [8] and Smith et al [10] proposed decentralized control approaches to allow realtime and incremental allocation process, but they may require a great deal of computation and training time, especially to deal with large environments.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration

et al. 2023

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Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible for the agents to visit. In this way, map segmentation and target allocation can be performed continually in real-time to efficiently explore the environment. To evaluate our proposed model, we conduct extensive experiments on map segmentation and multi-agent exploration. The results show that the proposed method can produce more accurate and meaningful segments leading to a higher level of efficiency in exploring the environment. Furthermore, the robustness tests by adding noises to the environments were conducted to simulate the performance of our model in the realworld environment. The results demonstrate the robustness of our model in map segmentation and multiagent environment exploration. INDEX TERMSAutonomous agents, Intelligent agents, Multi-agent systems, agent-based modeling, image segmentation

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“…Decentralized approaches can be further classified into two types: distributed approaches (for e.g. [48,90,91,103,[128][129][130][131]) in which all robots are equivalent with respect to their responsibility to coordinate, and hierarchical approaches which are locally centralized. Distributed coordination requires a distributed MRS, in which the system is composed of robots that are independent to take decisions with respect to each other.…”

Section: Decentralized Coordinationmentioning

confidence: 99%

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

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Real-time distributed non-myopic task selection for heterogeneous robotic teams

Cited by 12 publications

References 37 publications

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

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