Multi-robot Task Allocation for Search and Rescue Missions

Hussein, Ahmed; Adel, Mohamed; Bakr, Mohamed; Shehata, Omar M.; Khamis, Alaa

doi:10.1088/1742-6596/570/5/052006

Cited by 34 publications

(15 citation statements)

References 3 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%

“…For instance, in [128], Kurdi et al present a task allocation algorithm for multi-UAV SAR systems inspired by locust insects. Active perception techniques have also been incorporated in multi-robot planning algorithms in existing works [129], [130] An early work in multi-robot task allocation for SAR missions was presented by Hussein et al [122], with a market-based approach formulated as a multiple traveling salesman problem. The authors applied their algorithm to real robots with simulated victim locations that the robots had to divide among themselves and visit.…”

Section: A Multi-robot Task Allocationmentioning

confidence: 99%

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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%

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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“…There are several pioneering works that addressed MRTA problems, using market-based techniques, such as: M+ [3], First-Price Auctions [84], Dynamic Role Assignment [6], MURDOCH [23], Traderbots [13] and DEMiR-CF [52]. Authors of works in [25], [48], [79] proposed three different solutions to solve the MRTA problem in the field of search and rescue missions. Also, coalition formation were used to address this problem [71], [73].…”

Section: A Market-based Approachesmentioning

confidence: 99%

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

2020

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We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be N P-hard even with small numbers of robots and tasks. The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.INDEX TERMS Ant colony system, consensus-based bundle algorithm, coordination, multi-robot systems, multi-robot task allocation problem, resolution of conflicts.

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“…Current approaches can roughly be classified into two groups: (1) Approaches that try to map principles found in nature to the development of systems consisting of a multitude of simple robots (called swarm robotic systems [132]), and (2) approaches that use traditional AI techniques (e.g., planning, reasoning, learning, scheduling, etc.) [14,148,149]. Approaches of group (1) are characterized by the fact that agents only have very limited capabilities and are highly specialized for certain tasks, which limits their applicability for ScORe missions, as different ScORe tasks typically require agents with heterogeneous capabilities.…”

Section: Related Workmentioning

confidence: 99%

Multipotent Systems: Combining Planning, Self-Organization, and Reconfiguration in Modular Robot Ensembles

Kosak

Wanninger

Hoffmann

et al. 2018

Sensors

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Mobile multirobot systems play an increasing role in many disciplines. Their capabilities can be used, e.g., to transport workpieces in industrial applications or to support operational forces in search and rescue scenarios, among many others. Depending on the respective application, the hardware design and accompanying software of mobile robots are of various forms, especially for integrating different sensors and actuators. Concerning this design, robots of one system compared to each other can be classified to exclusively be either homogeneous or heterogeneous, both resulting in different system properties. While homogeneously configured systems are known to be robust against failures through redundancy but are highly specialized for specific use cases, heterogeneously designed systems can be used for a broad range of applications but suffer from their specialization, i.e., they can only hardly compensate for the failure of one specialist. Up to now, there has been no known approach aiming to unify the benefits of both these types of system. In this paper, we present our approach to filling this gap by introducing a reference architecture for mobile robots that defines the interplay of all necessary technologies for achieving this goal. We introduce the class of robot systems implementing this architecture as multipotent systems that bring together the benefits of both system classes, enabling homogeneously designed robots to become heterogeneous specialists at runtime. When many of these robots work together, we call the structure of this cooperation an ensemble. To achieve multipotent ensembles, we also integrate reconfigurable and self-descriptive hardware (i.e., sensors and actuators) in this architecture, which can be freely combined to change the capabilities of robots at runtime. Because typically a high degree of autonomy in such systems is a prerequisite for their practical usage, we also present the integration of necessary mechanisms and algorithms for achieving the systems’ multipotency. We already achieved the first results with robots implementing our approach of multipotent systems in real-world experiments as well as in a simulation environment, which we present in this paper.

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Multi-robot Task Allocation for Search and Rescue Missions

Cited by 34 publications

References 3 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 Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

Multipotent Systems: Combining Planning, Self-Organization, and Reconfiguration in Modular Robot Ensembles

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