TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response

Kruijff-Korbayová, Ivana; Colas, Francis; Gianni, Mario; Pirri, Fiora; Greeff, Joachim de; Hindriks, Koen V.; Neerincx, Mark A.; Ögren, Petter; Svoboda, Tomáš; Worst, Rainer

doi:10.1007/s13218-015-0352-5

Cited by 62 publications

(43 citation statements)

References 51 publications

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“…This section presents the results we obtained with an implementation in 3D. We validated the proposed strategy on the TRADR UGV robots [41] (cfr. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, nimbro network allows to fully leverage UDP and TCP protocols in order to control bandwidth consumption and avoid network congestions. This capability along with a comparative testing of different ROS multi-master architectures made the TRADR consortium adopt nimbro network [41,72,73].…”

Section: Real-world Experimentsmentioning

confidence: 99%

“…During this research and the TRADR experience [41], we learnt the following main lessons through numerous real world deployments.…”

Section: Lesson Learnt In Real World Deploymentmentioning

confidence: 99%

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3D multi-robot patrolling with a two-level coordination strategy

et al. 2019

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Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and realworld experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.

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“…This section presents the results we obtained with an implementation in 3D. We validated the proposed strategy on the TRADR UGV robots [41] (cfr. Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Real-world Experimentsmentioning

confidence: 99%

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3D multi-robot patrolling with a two-level coordination strategy

et al. 2019

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“…V rf * ← gpvar(X * ) ▷ return the diagonal 5: for all x in X * do 6: return X is * 13: end procedure V. EXPERIMENTAL EVALUATION In the following we describe the experimental setup showed in Fig. 1 and the experimental scenarios.…”

Section: B Surface Explorationmentioning

confidence: 99%

“…The first row of Fig. 5 shows the the point cloud representation of the full 6 Using PCL, Eigen, ROS, Kinova SDK box on the VRS together with its uncertainty distribution generated by the GRF and the process of action selection with the selected Delaunay triangles. Second row shows the evolution of the internal representation (computed offline) during each interactions.…”

Section: A Experimental Setupmentioning

confidence: 99%

Active exploration using Gaussian Random Fields and Gaussian Process Implicit Surfaces

Caccamo¹,

Bekiroglu²,

Ek³

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demonstrate how to use the online framework for object detection and terrain classification.

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An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environments

Krátký

Petráček

Báča

et al. 2021

Journal of Field Robotics

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This paper introduces an autonomous system employing multirotor unmanned aerial vehicles for fast 3D exploration and inspection of vast, unknown, dynamic, and complex environments containing large open spaces as well as narrow passages.The system exploits the advantage of small-size aerial vehicles capable of carrying all necessary sensors and computational power while providing full autonomy and mobility in constrained unknown environments. Particular emphasis is put on the robustness of the algorithms with respect to challenging real-world conditions and the real-time performance of all algorithms that enable fast reactions to changes in environment and thus also provide effective use of limited flight time. The system F I G U R E 1 The proposed system operating in a simulated disastrous scenario during the Urban Circuit of the Subterranean Challenge. The figure shows the unmanned aerial vehicle platform as a part of a multirobot team deployed within open spaces (a) and confined corridors (b) of the unfinished nuclear power plant in the

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TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response

Cited by 62 publications

References 51 publications

3D multi-robot patrolling with a two-level coordination strategy

3D multi-robot patrolling with a two-level coordination strategy

Active exploration using Gaussian Random Fields and Gaussian Process Implicit Surfaces

An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environments

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