Energy based path planning for a novel cabled robotic system

Borgstrom, Per Henrik; Singh, Amarjeet; Jordan, Brett; Sukhatme, G.; Batalin, Maxim; Kaiser, William J.

doi:10.1109/iros.2008.4651019

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…We used the orienteering heuristic proposed in [4] to implement SVA. This method has been empirically found to be one of the best orienteering heuristics [2]. It consists of two steps, where at the first step (initialization step) multiple paths are constructed by a greedy approach based on distance, while ignoring the collected reward.…”

Section: Methodsmentioning

confidence: 99%

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Efficient viewpoint assignment for urban texture documentation

Shirani-Mehr

Banaei-Kashani

Shahabi

2009

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. We have proposed to implement PTD in two steps [10]. At the first step, termed viewpoint selection, a minimum number of points in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we focus on the viewpoint assignment problem. We first prove that this problem is an NP-hard problem, and therefore, the optimal solution for viewpoint assignment fails to scale as the extent of the urban environment and the number of participating users grow. Subsequently, we propose a family of heuristics for efficient viewpoint assignment to reduce the assignment running time while ensuring an almost complete texture collection. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.

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Section: Methodsmentioning

confidence: 99%

“…We prove the lemma by providing a polynomial time reduction from the orienteering problem 2 . Towards that end, we prove that given an instance of orienteering, denoted by O, there exists an instance of SVA, denoted by S, such that the solution to S can be converted to the solution of O in polynomial time.…”

Section: Which Has the Running Time Of O((|v |B)mentioning

confidence: 99%

Efficient viewpoint assignment for urban texture documentation

Shirani-Mehr

Banaei-Kashani

Shahabi

2009

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…In the context of sensor networks, adaptive sampling has mainly been introduced to (a) maintain high resolution while covering large regions of space using mobile sensors, e.g., light sampling with Networked Infomechanical Systems (NIMS) [4], or to (b) reduce approximation errors with additional samples taken by mobile sensors, e.g., weather forecasting with autonomous UAVs [7]. Compared to these approaches, we do not seek to improve resolution with a reduced number of sensors, but to maintain utility of measurements in a changing environment.…”

Section: Adaptive Sampling In Sensor Networkmentioning

confidence: 99%

“…In terms of architecture, one or several detection modules are attached to each virtual sensor 4 . For example, the range of each measurement value can be checked and if some are found to be out-of-bounds the OutOfBounds predicate is set.…”

Section: System Architecturementioning

confidence: 99%

Meeting ecologists' requirements with adaptive data acquisition

Chang

Bonnet

2010

Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems

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Ecologists instrument ecosystems to collect time series representing the evolution in time and space of relevant abiotic and biotic factors. Sensor networks promise to improve on existing data acquisition systems by interconnecting stand-alone measurement systems into virtual instruments. Such ecological sensor networks, however, will only fulfill their potential if they meet the scientists requirements. In an ideal world, an ecologist expresses requirements in terms of a target dataset, which the sensor network then actually collects and stores. In fact, failures occur and interesting events happen, making uniform systematic ecosystem sampling neither possible nor desirable. Today, these anomalous situations are handled as exceptions treated by technicians that receive an alert at deployment time. In this paper, we detail how ecological sensor networks can maximize the utility of the collected datasets in a changing environment. More specifically, we present the design of a controller that continuously maintains its state based on the data obtained from the sensor network (as well as external systems), and configures motes with parameters that satisfy a constraint optimization problem derived from the current state, the system requirements, and the scientist requirements. We describe our implementation, discuss its scalability, and discuss its performance in the context of two case studies.

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“…Experiments were conducted on an actual robotic system for a critical application of monitoring light intensity in the forest understory. Networked InfoMechanical System for Planar actuation (NIMS-PL), a four cable based robotic system, was used for performing the system experiments [11]. NIMS-PL consists of four tension controlled cables capable of actuating a generic sensor to provide planar spatial coverage.…”

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confidence: 99%

Modeling and decision making in spatio-temporal processes for environmental surveillance

Singh

Ramos

Whyte

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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I. MOTIVATION AND METHODOLOGYA broad class of environmental monitoring applications, including meteorology and climatology, epidemiology, ecology, demography, forestry, fishery and others, require distributed sensing capabilities [1] due to the dynamics exhibited in both space and time. Understanding and modeling such complex space time dynamics with only static sensors would require an impractically large number of sensors to be distributed across the complete spatial extent of the observed environment. Mobile robots equipped with sensors offer an alternative to a network of static sensing elements for high spatial coverage but at the cost of increased delay (sampling latency). Several path planning approaches have been proposed in the literature to adaptively sample the environment to reduce the latency while still providing high fidelity sampling [2]- [5].At the core of each path planning approach is a model representing the space-time dependencies. Learning such complex and non-deterministic spatio-temporal dynamics, for accurate predictions, is challenging. A typical modeling procedure adopted in the field of environmental science is to manually specify partial differential equations governing the behavior of the observed phenomena [6]. This, however, relies on experts and usually requires time consuming validation experiments [7].In this work we consider a machine learning approach to the problem where a spatio-temporal Gaussian process model is learned through an optimization procedure. The model is then used for path planning. We propose a path planning method in continuous domain that can exploit the Gaussian Process based modeling to guide informative sensing. Our proposed greedy path planning algorithm uses information gain as the objective function. We assume that X t = (s 1 ; t 1 ), . . . , (s N ; t N ) be the training set of N observation locations, available to learn the hyper-parameters of the covariance function. With the learned hyper-parameters, the trained model is used for testing on the spatio-temporal dataset. We define a timestep to be an instance of the environment during which the observed phenomena is assumed to be static.A major difficulty for modeling spatio-temporal stochastic processes with GPs is the definition of a valid covariance function that can accurately account for space-time dependencies. We study a generic approach for creating several classes of valid non-stationary, spatio-temporal GP models. After learning the parameters associated with the corresponding covariance model, path planning is performed in continuous domain, incrementally adding new data points to the model. We present extensive empirical evaluation comparing several classes of GP models using different real world sensing datasets. We validate the proposed methodology of model learning and path planning using a Networked Info Mechanical System (NIMS, a tethered robotic system). A detailed presentation of this study was published in the proceedings of ICRA 2010.

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Energy based path planning for a novel cabled robotic system

Cited by 15 publications

References 15 publications

Efficient viewpoint assignment for urban texture documentation

Efficient viewpoint assignment for urban texture documentation

Meeting ecologists' requirements with adaptive data acquisition

Modeling and decision making in spatio-temporal processes for environmental surveillance

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