Hierarchical Distributed Task Allocation for Multi-robot Exploration

Hawley, John K.; Butler, Zack

doi:10.1007/978-3-642-32723-0_32

Cited by 11 publications

(4 citation statements)

References 11 publications

(21 reference statements)

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“…Generally speaking, our method found more scenarios with all the survivors rescued across different numbers of vehicles and survivors. For the first two cases, i.e., (6,12) and (8,16), in which the values in the brackets denote the number of vehicles and survivors, respectively, the CBBA method was only able to find one scenario with all the survivors rescued (scenarios 10 and 3, respectively). The situation became even more worse for the last four cases when larger numbers of vehicles and survivors involved, i.e., (10,20), (12,24), (14,28), and (16,32), where none of the scenarios has been found with all the survivors rescued.…”

Section: B Simulation Resultsmentioning

confidence: 99%

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A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario

Zhao

Meng

Chung

2016

IEEE Trans. Cybern.

194

124

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Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle's task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm.

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Section: B Simulation Resultsmentioning

confidence: 99%

“…These tasks may be known by the vehicles before task execution stage or may be dynamically appeared during task execution [7]. For example, a team of autonomous vehicles are executing an exploration mission [8]. The aim is thus to locate and visit a number of predetermined targets in a partially unknown terrain.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario

Zhao

Meng

Chung

2016

IEEE Trans. Cybern.

194

124

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“…Though autonomous exploration has been an active field of research for some time, finding a single optimal path to explore an entire environment has been found to be NP-hard [5], [6]. For this reason, current exploration strategies tend to approach exploration in a greedy fashion.…”

Section: Related Workmentioning

confidence: 99%

Efficient neighbourhood-based information gain approach for exploration of complex 3D environments

Quin

Paul

Alempijevic

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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This paper presents an approach for exploring a complex 3D environment with a sensor mounted on the end effector of a robot manipulator. In contrast to many current approaches which plan as far ahead as possible using as much environment information as is available, our approach considers only a small set of poses (vector of joint angles) neighbouring the robot's current pose in configuration space. Our approach is compared to an existing exploration strategy for a similar robot. Our results demonstrate a significant decrease in the number of information gain estimation calculations that need to be performed, while still gathering an equivalent or increased amount of information about the environment.

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“…Experimental results show that the auction-based coordination methods (as expected) outperform the uncoordinated methods. An extension of such works is the approach of Hawley and Butler (2013), who propose an auction-based coordination method not only for task assignment, but also for coalition formation, when there are more robots than candidate locations.…”

Section: Coordination Methodsmentioning

confidence: 98%

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Cipolleschi

Giusto

et al. 2015

Auton Robot

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Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like 'rooms' and 'corridors') with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for exam-B Alberto Quattrini Li ple, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.

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Hierarchical Distributed Task Allocation for Multi-robot Exploration

Cited by 11 publications

References 11 publications

A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario

A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario

Efficient neighbourhood-based information gain approach for exploration of complex 3D environments

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

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