Sampling-based robotic information gathering algorithms

Hollinger, Geoffrey A.; Sukhatme, Gaurav S.

doi:10.1177/0278364914533443

Cited by 262 publications

(220 citation statements)

References 34 publications

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“…Recent work has begun to explore sampling-based methods [11]. Numerous applications of information-based planning involve UAVs [10,15,21,24,25,27].…”

Section: Related Workmentioning

confidence: 99%

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Cliff

Fitch

Sukkarieh

et al. 2015

Robotics: Science and Systems XI

112

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Abstract-The application of autonomous robots to efficiently locate small wildlife species has the potential to provide significant ecological insights not previously possible using traditional landbased survey techniques, and a basis for improved conservation policy and management. We present an approach for autonomously localizing radio-tagged wildlife using a small aerial robot. We present a novel two-point phased array antenna system that yields unambiguous bearing measurements and an associated uncertainty measure. Our estimation and informationbased planning algorithms incorporate this bearing uncertainty to choose observation points that improve confidence in the location estimate. These algorithms run online in real time and we report experimental results that show successful autonomous localization of stationary radio tags and live radio-tagged birds.

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“…Recent work has begun to explore sampling-based methods [11]. Numerous applications of information-based planning involve UAVs [10,15,21,24,25,27].…”

Section: Related Workmentioning

confidence: 99%

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Cliff

Fitch

Sukkarieh

et al. 2015

Robotics: Science and Systems XI

112

View full text Add to dashboard Cite

show abstract

“…This leads the robots to locally optimize the information gain, a strategy known as "information surfing" [13]. While planning over a longer time horizon may be preferable in some scenarios [6], [19]- [21], the limited information provided by a binary sensor would require such a long horizon as to be computationally infeasible in practice.…”

Section: Controlmentioning

confidence: 99%

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Dames

Schwager

Rus

et al. 2016

IEEE Robot. Autom. Lett.

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Abstract-Magnetic Anomaly Detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

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“…These works find locally-optimal solutions to the planning problem and provide promising results for robotics applications, especially point-to-point planning queries. Work in path planning for information gathering [12] and active SLAM systems [13][14][15] focused more on the interaction between planning and SLAM, and how the performance and efficiency of SLAM is improved with intelligent decisions regarding which paths to travel.…”

Section: A Related Workmentioning

confidence: 99%

Risk aversion in belief-space planning under measurement acquisition uncertainty

Chaves

Walls

Galceran

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-This paper reports on a Gaussian belief-space planning formulation for mobile robots that includes random measurement acquisition variables that model whether or not each measurement is actually acquired. We show that maintaining the stochasticity of these variables in the planning formulation leads to a random belief covariance matrix, allowing us to consider the risk associated with the acquisition in the objective function. Inspired by modern portfolio theory and utility optimization, we design objective functions that are risk-averse, and show that risk-averse planning leads to decisions made by the robot that are desirable when operating under uncertainty. We show the benefit of this approach using simulations of a planar robot traversing an uncertain environment and of an underwater robot searching for loopclosure actions while performing visual SLAM.

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Sampling-based robotic information gathering algorithms

Cited by 262 publications

References 34 publications

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Risk aversion in belief-space planning under measurement acquisition uncertainty

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