Coordinated multi-robot exploration: Out of the box packages for ROS

Andre, Torsten; Neuhold, Daniel; Bettstetter, Christian

doi:10.1109/glocomw.2014.7063639

Cited by 27 publications

(14 citation statements)

References 14 publications

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“…The second one is more precise because it depends on the real estimation of the amount of information gained when visiting this state, but it requires more computation. In our case, the information gain I(f ij ) is the number of unknown voxels that are adjacent to this target [26].…”

Section: B the Exploration Processmentioning

confidence: 99%

Cooperative exploration strategy for micro-aerial vehicles fleet

Mahdoui¹,

Frémont²,

Natalizio³

2017

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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In this paper, the problem of the exploration of an unknown environment by deploying a fleet of Micro-Aerial Vehicles (MAV) is considered. As a single robot has already proven its efficiency for this task, the challenge is to extend it to a multi-robots system to reduce the exploration time. For this purpose, a cooperative navigation strategy is proposed based on a specific utility function and inter-robots data exchange. The novelty comes from the exchange of the frontiers points instead of maps, which allows to reduce computation and data amount within the network. The proposed system has been implemented and tested under ROS using the Gazebo simulator. The results demonstrate that the proposed navigation strategy efficiently spreads robots over the environment for a faster exploration.

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Section: B the Exploration Processmentioning

confidence: 99%

Cooperative exploration strategy for micro-aerial vehicles fleet

Mahdoui¹,

Frémont²,

Natalizio³

2017

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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“…Adding additional robots slows down exploration due to robot-to-robot interference. Also, mapping errors and additional processing time for increased number of robots may slow down exploration time [4]. Hayes suggests a model to approximate team sizes and their impact on search tasks [15].…”

Section: General Performance Comparisonmentioning

confidence: 99%

“…While they influence system behavior, the weights have no impact on the comparison of the strategies. The connectivity is approximated by the distances between robots and the well-known simple path loss model (4) and parameterized according to real-world measurements in an industrial building [3]. The path loss exponent γ is set to 3.8, the reference path loss is PL(d 0 ) = 45 dB at the reference distance d 0 = 2 m. The normally distributed random variable X σ , modeling shadowing, has a variance of 10 dB.…”

Section: Simulation Modelmentioning

confidence: 99%

“…We implement a dedicated simulator. Simulation of the strategies using the Robot Operating System (ROS) and publicly available ROS packages for coordinated multi-robot exploration [4] fail to perform this task. The mapping and map merging for large environ-ments are not yet robust enough to obtain meaningful results with respect to the local collaboration strategies.…”

Section: Simulation Modelmentioning

confidence: 99%

“…The robot requesting collaboration is called r C , i.e., r 3 ≡ r C . The robots receiving the request have to determine their suitability for the collaboration: r 1 has to decide whether to abort the exploration of a room with no more paths to follow; r 4 and r 5 partition the communication network because they are the only robots connecting r 6 ; r 6 has the longest distance to travel.…”

Section: Introductionmentioning

confidence: 99%

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Collaboration in Multi-Robot Exploration: To Meet or not to Meet?

Andre

Bettstetter

2015

J Intell Robot Syst

Self Cite

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Work on coordinated multi-robot exploration often assumes that all areas to be explored are freely accessible. This common assumption does not always hold, especially not in search and rescue missions after a disaster. Doors may be closed or paths blocked detaining robots from continuing their exploration beyond these points and possibly requiring multiple robots to clear them. This paper addresses the issue how to coordinate a multi-robot system to clear blocked paths. We define local collaborations that require robots to collaboratively perform a physical action at a common position. A collaborating robot needs to interrupt its current exploration and move to a different location to collaboratively clear a blocked path. We raise the question when to collaborate and whom to collaborate with. We propose four strategies as to when to collaborate. Two obvious strategies are to collaborate immediately or to postpone any collaborations until only blocked paths are left. The other two strategies make use of heuristics based on building patterns. While no single strategy behaves optimal in all scenarios, we show that the heuristics decrease T. Andre ( ) · C. Bettstetter Networked and Embedded Systems, AlpenAdria-Universität Klagenfurt, Lakeside Park B02a, Klagenfurt, Austria e-mail: torsten.andre@aau.at C. Bettstetter Lakeside Labs GmbH, Lakeside Park B04b, Klagenfurt, Austria the time required to explore unknown environments considering blocked paths.

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Multiagent robotic systems and exploration algorithms: Applications for data collection in construction sites

Prieto,

Giakoumidis,

García de Soto

2024

Journal of Field Robotics

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The construction industry has been notoriously slow to adopt new technology and embrace automation. This has resulted in lower efficiency and productivity compared to other industries where automation has been widely adopted. However, recent advancements in robotics and artificial intelligence offer a potential solution to this problem. In this study, a methodology is proposed to integrate multirobotic systems in construction projects with the aim of increasing efficiency and productivity. The proposed approach involves the use of multiple robot and human agents (HA) working collaboratively to complete a construction task. The methodology was tested through a case study that involved 3D digitization of a small, occluded space using two robots and one HA. The results show that integrating multiagent robotic systems (MARS) in construction can effectively overcome challenges and complete tasks efficiently. The implications of this study suggest that MARS could revolutionize the industry.

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Coordinated multi-robot exploration: Out of the box packages for ROS

Cited by 27 publications

References 14 publications

Cooperative exploration strategy for micro-aerial vehicles fleet

Cooperative exploration strategy for micro-aerial vehicles fleet

Collaboration in Multi-Robot Exploration: To Meet or not to Meet?

Multiagent robotic systems and exploration algorithms: Applications for data collection in construction sites

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