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56
Persistent ocean monitoring with underwater gliders: Towards accurate reconstruction of dynamic ocean processes
- In Proceedings of the International Conference on Robotics and Automation
, 2011
"... Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. ..."
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Cited by 33 (16 self)
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Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high-value data. More specifically, this paper proposes a path planning algorithm and aspeedcontrolalgorithmforunderwatergliders,whichtogethergiveinformativetrajectoriesfortheglider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed
Uncertainty-driven view planning for underwater inspection
- In Proc. IEEE Int. Conf. Robotics and Automation
, 2012
"... Abstract — We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). In such scenarios, the goal is to construct an accurate 3D model of the structure and to detect any anomalies (e.g., foreign objects or deformations). ..."
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Cited by 17 (6 self)
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Abstract — We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). In such scenarios, the goal is to construct an accurate 3D model of the structure and to detect any anomalies (e.g., foreign objects or deformations). We propose a method for constructing 3D meshes from sonarderived point clouds that provides watertight surfaces, and we introduce uncertainty modeling through non-parametric Bayesian regression. Uncertainty modeling provides novel cost functions for planning the path of the AUV to minimize a metric of inspection performance. We draw connections between the resulting cost functions and submodular optimization, which provides insight into the formal properties of active perception problems. In addition, we present experimental trials that utilize profiling sonar data from ship hull inspection. I.
Informative Path Planning for an Autonomous Underwater Vehicle
"... Abstract — We present a path planning method for autonomous underwater vehicles in order to maximize mutual information. We adapt a method previously used for surface vehicles, and extend it to deal with the unique characteristics of underwater vehicles. We show how to generate near-optimal paths wh ..."
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Cited by 15 (6 self)
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Abstract — We present a path planning method for autonomous underwater vehicles in order to maximize mutual information. We adapt a method previously used for surface vehicles, and extend it to deal with the unique characteristics of underwater vehicles. We show how to generate near-optimal paths while ensuring that the vehicle stays out of high-traffic areas during predesignated time intervals. In our objective function we explicitly account for the fact that underwater vehicles typically take measurements while moving, and that they do not have the ability to communicate until they resurface. We present field results from ocean trials on planning paths for a specific AUV, an underwater glider. A. Informative Path Planning I.
Multi-robot coordination with periodic connectivity
- In Robotics and Automation (ICRA), IEEE International Conference on
, 2010
"... Abstract — We consider the problem of multi-robot coordi-nation subject to constraints on the configuration. Specifically, we examine the case in which a mobile network of robots must search, survey, or cover an environment while remaining connected. While many algorithms utilize continual connectiv ..."
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Cited by 15 (2 self)
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Abstract — We consider the problem of multi-robot coordi-nation subject to constraints on the configuration. Specifically, we examine the case in which a mobile network of robots must search, survey, or cover an environment while remaining connected. While many algorithms utilize continual connectivity for such tasks, we relax this requirement and introduce the idea of periodic connectivity, where the network must regain connectivity at a fixed interval. We show that, in some cases, this problem reduces to the well-studied NP-hard multi-robot informative path planning (MIPP) problem, and we propose an online algorithm that scales linearly in the number of robots and allows for arbitrary periodic connectivity constraints. We prove theoretical performance guarantees and validate our approach in the coordinated search domain in simulation and in real-world experiments. Our proposed algorithm significantly outperforms a gradient method that requires continual connectivity and performs competitively with a market-based approach, but at a fraction of the computational cost. I.
Active Classification: Theory and Application to Underwater Inspection
"... Abstract We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian ac ..."
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Cited by 14 (4 self)
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Abstract We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80 % when compared to passive methods. 1
M.: Nonmyopic -Bayes-Optimal Active Learning of Gaussian Processes
- In: Proc. ICML
, 2014
"... A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic ✏-Bayes-optimal active learn-ing (✏-BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily devel ..."
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Cited by 10 (9 self)
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A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic ✏-Bayes-optimal active learn-ing (✏-BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily developed myopic/greedy algorithms or performed exploration and ex-ploitation separately. To perform active learning in real time, we then propose an anytime algo-rithm based on ✏-BAL with performance guaran-tee and empirically demonstrate using synthetic and real-world datasets that, with limited budget, it outperforms the state-of-the-art algorithms. 1.
On Active Target Tracking and Cooperative Localization for Multiple Aerial Vehicles
"... Abstract — This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target’s positio ..."
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Cited by 8 (6 self)
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Abstract — This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target’s position. We propose a gradient-based control approach that encompasses the three major optimum experimental-design criteria and relies on the Kalman filter for estimation fusion. We derive analytical lower and upper bounds on the target’s position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of sensors ’ accuracy and target’s dynamics on the steady-state performance of our coordination algorithm. Finally, in the case that the position of the vehicles is not perfectly known, we introduce a more challenging problem, termed Active Cooperative Localization and Multitarget Tracking (ACLMT). In this problem, the vehicles move in the 3-D space in order to maximize the accuracy of their own position estimate and that of multiple moving targets. I.
Stochastic Motion Planning for Robotic Information Gathering
"... 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 ..."
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Cited by 7 (1 self)
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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 Trees (RRTs) 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. I.
Sampling-based Motion Planning for Robotic Information Gathering
"... Abstract—We propose an incremental sampling-based mo-tion planning algorithm that generates maximally informative trajectories for guiding mobile robots to observe their envi-ronment. The goal is to find a trajectory that maximizes an information metric (e.g., variance reduction, information gain, o ..."
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Cited by 5 (1 self)
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Abstract—We propose an incremental sampling-based mo-tion planning algorithm that generates maximally informative trajectories for guiding mobile robots to observe their envi-ronment. 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 Gath-ering (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-of-concept field implementation on an autonomous surface vehicle performing a wireless signal strength monitoring task in a lake. I.
Sensor planning for a symbiotic UAV and UGV system for precision agriculture
- In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2013
"... Abstract-We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) to collect data for a precision agriculture application. The ground and aerial measurements collected by the system are used for estimating Nitrogen (N) levels across a farm field. These ..."
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Cited by 5 (2 self)
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Abstract-We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) to collect data for a precision agriculture application. The ground and aerial measurements collected by the system are used for estimating Nitrogen (N) levels across a farm field. These estimates in turn guide fertilizer application. The capability to apply the right amount of fertilizer at the right time can drastically reduce fertilizer usage which is desirable from an environmental and economic standpoint. We propose to use a symbiotic UAV and UGV system in which the UGV is capable of muling the UAV to various deployment locations. This would allow the system to overcome the short battery life of a typical UAV. Our goal is to estimate N levels over the field and assign each point in the field into classes indicating N-deficiency levels. Towards building such a system, the paper makes the following contributions: First, we present a method to identify points whose probability of being misclassified is above a threshold, termed as Potentially Mislabeled (PML). Second, we study the problem of planning the UAV path to visit the maximum number of PML points subject to its energy budget. The novelty of our formulation is the capability of the UGV to mule the UAV to deployment points. Third, we introduce a new path planning problem in which the UGV must take a measurement near each PML point visited by the UAV. The goal is to minimize the total time spent in traveling and taking measurements. For both problems, we present constant-factor approximation algorithms. Finally, we demonstrate the utility of the system and our algorithms with simulations which use manually collected data from the field as well as realistic energy models for the UAV and the UGV.
