Browsing by Author "Plonski, Patrick A."

Now showing 1 - 6 of 6
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    Approximation Algorithms for Tours of Orientation-varying View Cones
    (2018-02-16) Stefas, Nikolaos; Plonski, Patrick A.; Isler, Volkan
    This paper considers the problem of finding the shortest tour to cover a given set of inverted cone views with apex angle ? and height H when their apex points lie on a planar surface. This is a novel variant of the 3D Traveling Salesman Problem with intersecting Neighborhoods (TSPN) called Cone-TSPN. When the cones are allowed to tilt by an angle ? we have the tilted Cone-TSPN problem, to which we present a polynomial time approximation algorithm. We demonstrate through simulations that our algorithm can be implemented in a practical way and by exploiting the structure of the cones we can achieve shorter tours. Finally, we present results from covering a reflective surface (lake area) that shows the importance of selecting different view angles under strong sunlight specularities.
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    Environment and Solar Map Construction for Solar-Powered Mobile Systems
    (2015-04-16) Plonski, Patrick A.; Vander Hook, Joshua; Isler, Volkan
    Energy harvesting using solar panels can significantly increase the operational life of mobile robots. If a map of expected solar power is available, energy efficient paths can be computed. However, estimating this map is a challenging task, especially in complex environments. In this paper, we show how the problem of estimating solar power can be decomposed into the steps of magnitude estimation and solar classification. Then we provide two methods to classify a position as sunny or shaded: a simple data-driven Gaussian Process method, and a method which estimates the geometry of the environment as a latent variable. Both of these methods are practical when the training measurements are sparse, such as with a simple robot that can only measure solar power at its own position. We demonstrate our methods on simulated, randomly generated environments. We also justify our methods with measured solar data by comparing the constructed height maps with satellite images of the test environments, and in a cross-validation step where we examine the accuracy of predicted shadows and solar current.
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    Long-Term Search Through Energy Efficiency and Harvesting
    (2013-01-09) Noori, Narges; Plonski, Patrick A.; Renzaglia, Alessandro; Tokekar, Pratap; VanderHook, Joshua
    We study a search problem motivated by our ongoing work on finding radio-tagged invasive fish with an Autonomous Surface Vehicle (ASV). We focus on settings where the fish tend to move along the boundary of a lake. This setting allows us to formulate the problem as a one-dimensional search problem in which the searcher chooses between station keeping and moving so as to maximize the probability of finding the target in a given amount of time without violating its energy-budget. We model the movement of the target as a random-walk and present a closed-form solution for this search problem. Next, we investigate how long-term autonomy can be enabled by energy harvesting. In this case, the search strategy should incorporate the amount of solar energy available at a particular location and particular time. We show how this quantity can be predicted by estimating the geometry of the tree line along the shore. We then obtain the optimal strategy which maximizes the probability of finding the target by formulating the problem as finding the optimal strategy for a Markov Decision Process. Data collected from field experiments validate our approach.
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    Maintaining Connectivity in Environments with Obstacles
    (2010-01-29) Tekdas, Onur; Plonski, Patrick A.; Karnad, Nikhil
    Robotic routers (mobile robots with wireless communication capabilities) can create an adaptive wireless network and provide communication services for mobile users on-demand. Robotic routers are especially appealing for applications in which there is a single user whose connectivity to a base station must be maintained in an environment that is large compared to the wireless range. In this paper, we study the problem of computing motion strategies for robotic routers in such scenarios, as well as the minimum number of robotic routers necessary to enact our motion strategies. Assuming that the routers are as fast as the user, we present an optimal solution for cases where the environment is a simply-connected polygon, a constant factor approximation for cases where the environment has a single obstacle, and an O(h) approximation for cases where the environment has h circular obstacles. The O(h) approximation also holds for cases where the environment has h arbitrary polygonal obstacles, provided they satisfy certain geometric constraints - e.g. when the set of their minimum bounding circles is disjoint.
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    Navigation Around an Unknown Obstacle for Autonomous Surface Vehicles Using a Forward-Facing Sonar
    (2015-04-16) Plonski, Patrick A.; Vander Hook, Joshua; Peng, Cheng; Noori, Narges; Isler, Volkan
    A robotic boat is moving between two points when it encounters an obstacle of unknown size. The boat must find a short path around the obstacle to resume its original course. How should the boat move when it can only sense the proximity of the obstacle, and does not have prior information about the obstacle’s size? We study this problem for a robotic boat with a forward-facing sonar. We study two versions of the problem. First, we solve a simplified case when the obstacle is a rectangle of known orientation but unknown dimensions. Second, we study a more general case where an arbitrarily shaped obstacle is contained between two known parallel lines. We study the performance of the algorithms analytically using competitive analysis and present results from field experiments. The experimental setup is relevant for harbor patrol or autonomous navigation in shallow water.
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    Snakes in a Plant: 3D Reconstruction of Foliage using Tethered Active Contours
    (2018-06-15) Plonski, Patrick A.; Isler, Volkan
    We present a novel method for reconstructing 3D models of foliage from a pair of images. Leaves are especially challenging objects to reconstruct in natural settings because of the lack of distinct features. We present a novel method for simultaneously growing contours to detect leaf boundaries. We then compute the transformation between the leaves to generate disparity maps. We demonstrate the effectiveness of our method on challenging instances where standard reconstruction methods fail.