Field-Testing of a UAV-UGV Team for GNSS-Denied Navigation in Subterranean Environments

Gross, Jason N.; Petrillo, Matteo De; Beard, Jared J.; Nichols, Hayden; Swiger, Tommy; Watson, Ryan M.; Kirk, C.; Kiliç, Çağri; Hikes, Jacob; Upton, Emily; Ross, D. L.; Russell, Matt; Gu, Yu; Griffin, Christopher

doi:10.33012/2019.16912

Cited by 12 publications

(12 citation statements)

References 12 publications

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“…This paper contributes a new search planning algorithm that fuses the capabilities of two robots (UAV/UGV) to balance between environment exploration tasks while reducing the localization error for the UAV along the selected trajectory. This paper substantively builds upon our previous work [12], where the initial design and evaluation of the UAV localization system is presented.…”

Section: Introductionmentioning

confidence: 65%

“…In these cases, the planning algorithm is optimized to reduce the time to collect information [10] choosing different waypoints [11], especially if the same kind of robot and sensors are used. For example, to diversify information and to perform both autonomous search and rescue, we presented in previous work a UAV and UGV team for subterranean exploration [12]. The cooperation of multiple robots increases the ability to perform a wider variety of tasks.…”

Section: Introductionmentioning

confidence: 99%

“…To address some of these challenges, the approach presented in this work proposes to use active sensors on the UGV to localize the UAV with respect to the UGV in a manner that is not prone to drift, and with modalities that will work in a variety of lighting conditions. In particular, following on the approach considered in our initial work presented in [12]. With this UAV/UGV teaming configuration, the UGV has the advantage of additional load capacity and longer endurance to easily carry many sensors along with the significant computational power needed to use them in near real-time.…”

Section: Introductionmentioning

confidence: 99%

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Search Planning of a UAV/UGV Team with Localization Uncertainty in a Subterranean Environment

Petrillo¹,

Beard²,

Gu³

et al. 2021

Preprint

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We present a waypoint planning algorithm for an unmanned aerial vehicle (UAV) that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment. The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multi-sensor fusion of a fish-eye camera, 3D LIDAR, ranging radio, and a laser altimeter. Likewise, the trajectory planning of the UAV is conducted on the UGV, which is assumed to have a 3D map of the environment (e.g., from Simultaneous Localization and Mapping). The goal of the planning algorithm is to satisfy the mission's exploration criteria while reducing the localization error of the UAV by evaluating the belief space for potential exploration routes. The presented algorithm is evaluated in a relevant simulation environment where the planning algorithm is shown to be effective at reducing the localization errors of the UAV.

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Section: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Search Planning of a UAV/UGV Team with Localization Uncertainty in a Subterranean Environment

Petrillo¹,

Beard²,

Gu³

et al. 2021

Preprint

Self Cite

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“…Today, robots are active members in many industries: finding applications in scientific surveys [69], emergency relief [35], and capital maintenance [81], among others. As robots enter mainstream society, they are subject to increasingly unstructured environments.…”

Section: Motivation and Impactmentioning

confidence: 99%

“…In line with consideration for 3D environments, the need for multiple agents with varying capabilities is worthy of consideration. Consider a UAV/UGV team such as that in [35]. UAVs can rapidly search a region from the air.…”

Section: Future Workmentioning

confidence: 99%

Environment Search Planning Subject to High Robot Localization Uncertainty

Beard¹

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As robots find applications in more complex roles, ranging from search and rescue to healthcare and services, they must be robust to greater levels of localization uncertainty and uncertainty about their environments. Without consideration for such uncertainties, robots will not be able to compensate accordingly, potentially leading to mission failure or injury to bystanders. This work addresses the task of searching a 2D area while reducing localization uncertainty. Wherein, the environment provides low uncertainty pose updates from beacons with a short range, covering only part of the environment. Otherwise the robot localizes using dead reckoning, relying on wheel encoder and yaw rate information from a gyroscope. As such, outside of the regions with position updates, there will be unconstrained localization error growth over time. The work contributes a Belief Markov Decision Process formulation for solving the search problem and evaluates the performance using Partially Observable Monte Carlo Planning (POMCP). Additionally, the work contributes an approximate Markov Decision Process formulation and reduced complexity state representation. The approximate problem is evaluated using value iteration. To provide a baseline, the Google OR-Tools package is used to solve the travelling salesman problem (TSP). Results are verified by simulating a differential drive robot in the Gazebo simulation environment. POMCP results indicate planning can be tuned to prioritize constraining uncertainty at the cost of increasing path length. The MDP formulation provides consistently lower uncertainty with minimal increases in path length over the TSP solution. Both formulations show improved coverage outcomes.

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Challenges Associated with the Deployment of Autonomous Reconnaissance Systems on Future Battlefields

Nohel,

Stodola,

Zezula

et al. 2024

Lecture Notes in Computer Science

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Field-Testing of a UAV-UGV Team for GNSS-Denied Navigation in Subterranean Environments

Cited by 12 publications

References 12 publications

Search Planning of a UAV/UGV Team with Localization Uncertainty in a Subterranean Environment

Search Planning of a UAV/UGV Team with Localization Uncertainty in a Subterranean Environment

Environment Search Planning Subject to High Robot Localization Uncertainty

Challenges Associated with the Deployment of Autonomous Reconnaissance Systems on Future Battlefields

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