Reverdy, Paul B

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Now showing 1 - 6 of 6
  • Publication
    A drift-diffusion model for robotic obstacle avoidance
    (2015-08-01) Reverdy, Paul B; Koditschek, Daniel E; Ilhan, B. Deniz
    We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Peclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform. For more information: Kod*Lab
  • Publication
    Robotic Measurement of Aeolian Processes
    (2015-01-01) Jerolmack, Douglas J; Roberts, Sonia; Reverdy, Paul B; Lancaster, Nick; Nikolich, George; Shipley, Thomas F; Koditschek, Daniel E.; van Pelt, Scott; Zobeck, Ted
    Measurements used to study wind shear stress and turbulence, surface roughness, sand flux, and dust emissions are typically obtained from stationary instrumentation, and are thus limited spatially. They are also dependent on deployment of instrumentation for specific events and thus the are limited temporally. We have been adapting a rough-terrain legged robot capable of rapidly traversing desert terrain to serve as a semi-autonomous, reactive mobile sensory platform (RHex [1]), which would not share these limitations. We report on early trials of the robotic platform at the Jornada LTER and White Sands National Monument to test the feasibility of gathering measurements of airflow and rates of particle transport on a dune, assessing the role of roughness elements such as vegetation in modifying the wind shear stresses incident on the surface, and estimating erosion susceptibility in an arid soil. The robot not only serves as a mobile platform for science instruments; it can also perform controlled “kick tests” to locally examine soil strength. We outline a strategy for mapping soil erodibility and its controlling parameters using the unique capabilities of RHex, and the implications for understanding erosion and dust emission from complex terrain.
  • Publication
    Spatial Sampling Strategies with Multiple Scientific Frames of Reference
    (2017-06-01) Reverdy, Paul B; Koditschek, Daniel E; Shipley, Thomas F
    We study the spatial sampling strategies employed by field scientists studying aeolian processes, which are geophysical interactions between wind and terrain. As in geophysical field science in general, observations of aeolian processes are made and data gathered by carrying instruments to various locations and then deciding when and where to record a measurement. We focus on this decision-making process. Because sampling is physically laborious and time consuming, scientists often develop sampling plans in advance of deployment, i.e., employ an offline decision-making process. However, because of the unpredictable nature of field conditions, sampling strategies generally have to be updated online. By studying data from a large field deployment, we show that the offline strategies often consist of sampling along linear segments of physical space, called transects. We proceed by studying the sampling pattern on individual transects. For a given transect, we formulate model-based hypotheses that the scientists may be testing and derive sampling strategies that result in optimal hypothesis tests. Different underlying models lead to qualitatively different optimal sampling behavior. There is a clear mismatch between our first optimal sampling strategy and observed behavior, leading us to conjecture about other, more sophisticated hypothesis tests that may be driving expert decision-making behavior. For more information: Kod*lab
  • Publication
    Mobile Robots as Remote Sensors for Spatial Point Process Models
    (2016-10-01) Reverdy, Paul B; Koditschek, Daniel E
    Spatial point process models are a commonly-used statistical tool for studying the distribution of objects of interest in a domain. We study the problem of deploying mobile robots as remote sensors to estimate the parameters of such a model, in particular the intensity parameter lambda which measures the mean density of points in a Poisson point process. This problem requires covering an appropriately large section of the domain while avoiding the objects, which we treat as obstacles. We develop a control law that covers an expanding section of the domain and an online criterion for determining when to stop sampling, i.e., when the covered area is large enough to achieve a desired level of estimation accuracy, and illustrate the resulting system with numerical simulations.
  • Publication
    Ground robotic measurement of aeolian processes
    (2017-08-01) Qian, Feifei; Jerolmack, Douglas J; Lancaster, Nicholas; Nikolich, George; Reverdy, Paul B; Roberts, Sonia F; Shipley, Thomas F; Van pelt, Robert Scott; Zobeck, Ted M; Koditschek, Daniel E
    Models of aeolian processes rely on accurate measurements of the rates of sediment transport by wind, and careful evaluation of the environmental controls of these processes. Existing field approaches typically require intensive, event-based experiments involving dense arrays of instruments. These devices are often cumbersome and logistically difficult to set up and maintain, especially near steep or vegetated dune surfaces. Significant advances in instrumentation are needed to provide the datasets that are required to validate and improve mechanistic models of aeolian sediment transport. Recent advances in robotics show great promise for assisting and amplifying scientists’ efforts to increase the spatial and temporal resolution of many environmental measurements governing sediment transport. The emergence of cheap, agile, human-scale robotic platforms endowed with increasingly sophisticated sensor and motor suites opens up the prospect of deploying programmable, reactive sensor payloads across complex terrain in the service of aeolian science. This paper surveys the need and assesses the opportunities and challenges for amassing novel, highly resolved spatiotemporal datasets for aeolian research using partially-automated ground mobility. We review the limitations of existing measurement approaches for aeolian processes, and discuss how they may be transformed by ground-based robotic platforms, using examples from our initial field experiments. We then review how the need to traverse challenging aeolian terrains and simultaneously make high-resolution measurements of critical variables requires enhanced robotic capability. Finally, we conclude with a look to the future, in which robotic platforms may operate with increasing autonomy in harsh conditions. Besides expanding the completeness of terrestrial datasets, bringing ground-based robots to the aeolian research community may lead to unexpected discoveries that generate new hypotheses to expand the science itself. For more information: Kod*lab (http://kodlab.seas.upenn.edu/)
  • Publication
    A Dynamical System for Prioritizing and Coordinating Motivations
    (2018-01-01) Reverdy, Paul; Koditschek, Daniel E.
    We develop a dynamical systems approach to prioritizing multiple tasks in the context of a mobile robot. We take navigation as our prototypical task, and use vector field planners derived from navigation functions to encode control policies that achieve each individual task. We associate a scalar quantity with each task, representing its current importance to the robot; this value evolves in time as the robot achieves tasks. In our framework, the robot uses as its control input a convex combination of the individual task vector fields. The weights of the convex combination evolve dynamically according to a decision model adapted from the bio-inspired literature on swarm decision making, using the task values as an input. We study a simple case with two navigation tasks and derive conditions under which a stable limit cycle can be proven to emerge. While owing along the limit cycle, the robot periodically navigates to each of the two goal locations; moreover, numerical study suggests that the basin of attraction is quite large so that significant perturbations are recovered with a reliable return to the desired task coordination pattern. For more information: Kod*lab and http://www.paulreverdy.com/2018/05/11/motivation-dynamics-simulations/