Particle filter based information-theoretic active sensing

Ryan, Allison; Hedrick, J. Karl

doi:10.1016/j.robot.2010.01.001

Cited by 91 publications

(61 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent work has begun to explore sampling-based methods [11]. Numerous applications of information-based planning involve UAVs [10,15,21,24,25,27]. In previous work we explored multi-UAV constrained search [8] and UAV planning using formal methods [30,31].…”

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

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.

show abstract

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

“…While such algorithms have been shown to be useful for a range of domains, they typically rely on restrictive assumptions on the objective function and do not have guarantees on global optimality. Finite-horizon model predictive control methods [3,19] provide improvement over myopic techniques but also do not have performance guarantees beyond the horizon depth.…”

Section: Related Workmentioning

confidence: 99%

Sampling-based Motion Planning for Robotic Information Gathering

Hollinger

Sukhatme

2013

Robotics: Science and Systems IX

View full text Add to dashboard Cite

Abstract-We propose an incremental sampling-based motion planning algorithm that generates maximally informative trajectories for guiding mobile robots to observe their environment. The goal is to find a trajectory that maximizes an information metric (e.g., variance reduction, information gain, or mutual information) and also falls within a pre-specified budget constraint (e.g., fuel, energy, or time). Prior algorithms have employed combinatorial optimization techniques to solve these problems, but existing techniques are typically restricted to discrete domains and often scale poorly in the size of the problem. Our proposed Rapidly-exploring Information Gathering (RIG) algorithm extends ideas from Rapidly-exploring Random Graphs (RRGs) and combines them with branch and bound techniques to achieve efficient optimization of information gathering while also allowing for operation in continuous space with motion constraints. We provide a rigorous analysis of the asymptotic optimality of our approach, and we present several conservative pruning strategies for modular, submodular, and time-varying information objectives. We demonstrate that our proposed approach finds optimal solutions more quickly than existing combinatorial solvers, and we provide a proof-ofconcept field implementation on an autonomous surface vehicle performing a wireless signal strength monitoring task in a lake.

show abstract

“…However, these guarantees only hold in offline settings where new measurements are not incorporated as robots move. Ryan and Hedrick [16] minimized entropy over a receding horizon to track a mobile car with a fixed wing plane. We use dynamically simpler ground robots, but our approximations enable us to calculate control inputs for multiple robots in real-time.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to other work on predicting the motion of an uncooperative target [16,21] we use a Gaussian random walk for the process model: p(x t | x t−1 ) = N (x t ; x t−1 , σ 2 I) where x t−1 is the mean and σ 2 I is the covariance. We assume that the measurement model, p(z t | x t ), can be modeled as the true distance between the target and measuring robot perturbed by Gaussian noise.…”

Section: A Target Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Approximate Representations for Multi-Robot Control Policies that Maximize Mutual Information

Charrow¹,

Kumar²,

Michael³

2013

Robotics: Science and Systems IX

View full text Add to dashboard Cite

Abstract-We address the problem of controlling a small team of robots to estimate the location of a mobile target using non-linear range-only sensors. Our control law maximizes the mutual information between the team's estimate and future measurements over a finite time horizon. Because the computations associated with such policies scale poorly with the number of robots, the time horizon associated with the policy, and typical non-parametric representations of the belief, we design approximate representations that enable real-time operation. The main contributions of this paper include the control policy, an algorithm for approximating the belief state with provable error bounds, and an extensive study of the performance of these algorithms using simulations and real world experiments in complex, indoor environments.

show abstract

Particle filter based information-theoretic active sensing

Cited by 91 publications

References 19 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

Sampling-based Motion Planning for Robotic Information Gathering

Approximate Representations for Multi-Robot Control Policies that Maximize Mutual Information

Contact Info

Product

Resources

About