Adaptive teams of autonomous aerial and ground robots for situational awareness

Hsieh, M. Ani; Cowley, Anthony; Keller, James F.; Chaimowicz, Luiz; Grocholsky, Ben; Kumar, Vijay; Taylor, Camillo J.; Endo, Yoichiro; Arkin, Ronald C.; Jung, Boyoon; Wolf, Denis F.; Sukhatme, Gaurav S.; MacKenzie, Doug

doi:10.1002/rob.20222

Cited by 133 publications

(63 citation statements)

References 35 publications

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“…This work opens up the possibility of distributed sensors being used both for acquiring information and providing state information for controlling individual vehicles. Coordination of ground and air vehicles is discussed by Hsieh et al (2007) where benefits similar to those found in this work are identified, namely the ability of air vehicles to cover large areas at low resolution relatively rapidly, complements the ability of ground based robots to carry larger sensors to gain more accurate information about specific targets. This paper outlines the structure of the ground and aerial vehicles used to compete in the MOD grand challenge.…”

Section: Introductionmentioning

confidence: 62%

A multiple robot solution for urban reconnaissance

Janko

Turner

Cave-Ayland

et al. 2011

IJIDSS

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Coordinating aerial and ground based robotic platforms together with sensor payloads is described and some results are presented on the deployment of these robots in an urban environment for reconnaissance, in particular to find military threats. This work was done with the aim of competing in the UK MOD Grand Challenge competition in August 2008. This paper reviews the structure, control, coordination and integration of the robotic platforms for this event.

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Section: Introductionmentioning

confidence: 62%

A multiple robot solution for urban reconnaissance

Janko

Turner

Cave-Ayland

et al. 2011

IJIDSS

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“…In most related works, the aerial robot flies outdoors higher than 20 meters, and can be localized using GPS [7]- [9]. To the best of our knowledge, the work in [1] is the first to demonstrate how a MAV could assist a ground robot in close collaboration in mapping a damaged building indoors.…”

Section: Related Workmentioning

confidence: 99%

Air-ground localization and map augmentation using monocular dense reconstruction

Förster

Pizzoli

Scaramuzza

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of air-ground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-the-art approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features. Air-Ground Localization and Map Augmentation Using Monocular Dense ReconstructionChristian Forster, Matia Pizzoli, Davide ScaramuzzaAbstract-We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of airground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-theart approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features.

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“…Hsieh et al [8] deployed multiple UAVs and ground vehicles to locate and track human targets in large indoors and outdoors environments using a Simultaneous Localization and Mapping (SLAM) approach. Their two-phase search process consists of identifying targets visually within a map of the target region, which is constructed from aerial footage obtained using UAVs.…”

Section: Related Workmentioning

confidence: 99%

Multi-domain monitoring of marine environments using a heterogeneous robot team

Shkurti

Meghjani

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

118

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Abstract-In this paper we describe a heterogeneous multirobot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on realtime interaction with a remotely-located marine biologist.

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Adaptive teams of autonomous aerial and ground robots for situational awareness

Cited by 133 publications

References 35 publications

A multiple robot solution for urban reconnaissance

A multiple robot solution for urban reconnaissance

Air-ground localization and map augmentation using monocular dense reconstruction

Multi-domain monitoring of marine environments using a heterogeneous robot team

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