On-the-Fly Decentralized Tasking of Autonomous Vehicles

Tadewos, Tadewos G.; Shamgah, Laya; Karimoddini, Ali

doi:10.1109/cdc40024.2019.9029554

Cited by 16 publications

(7 citation statements)

References 16 publications

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“…Successive works have been presenting more decentralized methods. Decentralized task allocation algorithms for autonomous robots are very often based on market-based approaches and auction mechanisms to achieve consensus among the agents [122]- [125]. Both of this approaches have been extensively studied for the past two decades within the multi-robot and multi-agent systems communities [126], [127].…”

Section: A Multi-robot Task Allocationmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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Section: A Multi-robot Task Allocationmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

View full text Add to dashboard Cite

show abstract

“…[87] LTL [164], [100], [25], [88] GOAP [146] Other [159], [160], [182] [126] previously encountered. Following this process, rules can be constructed that may solve future problems by retrieving and reusing stored behaviors [46,119].…”

Section: Plannermentioning

confidence: 99%

“…Based on the same idea, [159] and [160] present their own algorithm for the automatic synthesis of BTs. This algorithm is combined with dynamic differential logic (DL), a verification tool used in [159] to ensure the safety of the generated BT.…”

Section: Planning and Analytic Designmentioning

confidence: 99%

“…This algorithm is combined with dynamic differential logic (DL), a verification tool used in [159] to ensure the safety of the generated BT. Then, DL is combined with a market-based auction algorithm and a coordinator to assign specific task to certain robots, in the multisystem scenario presented in [160]. In [126] a heuristic planner is used to create BTs in the form of Robust Logical-Dynamical Systems (RLDS).…”

Section: Planning and Analytic Designmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Behavior Trees in Robotics and AI

Iovino¹,

Scukins²,

Styrud³

et al. 2020

Preprint

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“…Successive works have been presenting more decentralized methods. Decentralized task allocation algorithms for autonomous robots are very often based on market-based approaches and auction mechanisms to achieve consensus among the agents [119]- [122]. Both of this approaches have been extensively studied for the past two decades within the multi-robot and multi-agent systems communities [123], [124].…”

Section: A Multi-robot Task Allocationmentioning

confidence: 99%

Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception

Queralta,

Taipalmaa,

Pullinen

et al. 2020

Preprint

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Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years. Across the different civil domains where robots can support human operators, one of the areas where they can have more impact is in search and rescue (SAR) operations. In particular, multi-robot systems have the potential to significantly improve the efficiency of SAR personnel with faster search of victims, initial assessment and mapping of the environment, real-time monitoring and surveillance of SAR operations, or establishing emergency communication networks, among other possibilities. SAR operations encompass a wide variety of environments and situations, and therefore heterogeneous and collaborative multirobot systems can provide the most advantages. In this paper, we review and analyze the existing approaches to multi-robot SAR support, from an algorithmic perspective and putting an emphasis on the methods enabling collaboration among the robots as well as advanced perception through machine vision and multi-agent active perception. Furthermore, we put these algorithms in the context of the different challenges and constraints that various types of robots (ground, aerial, surface or underwater) encounter in different SAR environments (maritime, urban, wilderness or other post-disaster scenarios). This is, to the best of our knowledge, the first review considering heterogeneous SAR robots across different environments, while giving two complimentary points of view: control mechanisms and machine perception. Based on our review of the state-of-the-art, we discuss the main open research questions, and outline our insights on the current approaches that have potential to improve the real-world performance of multi-robot SAR systems.

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On-the-Fly Decentralized Tasking of Autonomous Vehicles

Cited by 16 publications

References 16 publications

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

A Survey of Behavior Trees in Robotics and AI

Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception

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