@conference{Sung2014,

title = {Foldable joints for foldable robots},

author = {Cynthia Sung and Daniela Rus},

doi = {10.1007/978-3-319-23778-7_28},

year  = {2014},

date = {2014-06-15},

booktitle = {International Symposium on Experimental Robotics (ISER)},

pages = {421-433},

abstract = {Print-and-fold approaches to robot fabrication allow entire

robots to be produced using a single uniform process: fabricating them

in-plane and then folding them into their 3-D forms. Current efforts to

design print-and-fold robots have been limited by a lack of understanding

of what motions can be achieved by folding. In this paper, we introduce

fold patterns for three basic joints commonly used in robots, and we

show how the patterns can be changed to accommodate user-specified

ranges of motion. The joints are composed with each other to produce

joints with higher degrees of freedom and with rigid bodies to produce

entire foldable linkage mechanisms. We have folded our basic joints and

composed mechanisms, and they achieve the expected kinematics. We

have also printed control circuitry on and attached actuators directly

to three of our designs, demonstrating that it possible to print and fold

robots with many different kinematics.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{feldman2013idiary,

title = {iDiary: From GPS signals to a text-searchable diary},

author = {Dan Feldman and Andrew Sugaya and Cynthia Sung and Daniela Rus},

doi = {10.1145/2517351.2517366},

year  = {2013},

date = {2013-11-11},

booktitle = {11th ACM Conference on Embedded Networked Sensor Systems (SenSys)},

pages = {6},

publisher = {ACM},

abstract = {This paper describes a system that takes as input GPS data streams generated by users' phones and creates a searchable database of locations and activities. The system is called iDiary and turns large GPS signals collected from smartphones into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., "Where did I buy books?") and receive textual answers based on their GPS signals.



iDiary uses novel algorithms for semantic compression (known as coresets) and trajectory clustering of massive GPS signals in parallel to compute the critical locations of a user. Using an external database, we then map these locations to textual descriptions and activities so that we can apply text mining techniques on the resulting data (e.g. LSA or transportation mode recognition).



We provide experimental results for both the system and algorithms and compare them to existing commercial and academic state-of-the-art. This is the first GPS system that enables text-searchable activities from GPS data.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{sung2013joining,

title = {Joining unfoldings of 3-D surfaces},

author = {Cynthia Sung and Erik D. Demaine and Martin L. Demaine and Daniela Rus},

doi = {10.1115/DETC2013-12692},

year  = {2013},

date = {2013-08-04},

booktitle = {ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE)},

pages = {DETC2013-12692},

publisher = {ASME},

abstract = {Origami-based design methods enable complex devices to be fabricated quickly in plane and then folded into their final 3-D shapes. So far, these folded structures have been designed manually. This paper presents a geometric approach to automatic composition of folded surfaces, which will allow existing designs to be combined and complex functionality to be produced with minimal human input. We show that given two surfaces in 3-D and their 2-D unfoldings, a surface consisting of the two originals joined along an arbitrary edge can always be achieved by connecting the two original unfoldings with some additional linking material, and we provide an algorithm to generate this composite unfolding. The algorithm is verified using various surfaces, as well as a walking and gripping robot design.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{sung2013improving,

title = {Improving the performance of multi-robot systems by task switching},

author = {Cynthia Sung and Nora Ayanian and Daniela Rus},

doi = {10.1109/ICRA.2013.6630993},

year  = {2013},

date = {2013-05-06},

booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},

pages = {2999-3006},

publisher = {IEEE},

abstract = {We consider the problem of task assignment for a multi-robot system where each robot must attend to one or more queues of tasks. We assume that individual robots have no knowledge of tasks in the environment that are not in their queue. Robots in communication with each other may share information about active tasks and exchange queues to achieve lower cost for the system. We show that allowing this kind of task switching causes tasks to be completed more efficiently. In addition, we present conditions under which queues can be guaranteed to make progress, and we support these claims with simulation and experimental results. This work has potential applications in manufacturing, environmental exploration, and pickup-delivery tasks.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{feldman2012single,

title = {The single pixel GPS: Learning big data signals from tiny coresets},

author = {Dan Feldman and Cynthia Sung and Daniela Rus},

doi = {10.1145/2424321.2424325},

year  = {2012},

date = {2012-11-06},

booktitle = {20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS 2012)},

pages = {23-32},

publisher = {ACM},

abstract = {We present algorithms for simplifying and clustering patterns from sensors such as GPS, LiDAR, and other devices that can produce high-dimensional signals. The algorithms are suitable for handling very large (e.g. terabytes) streaming data and can be run in parallel on networks or clouds. Applications include compression, denoising, activity recognition, road matching, and map generation.



We encode these problems as (k, m)-segment mean problems. Formally, we provide (1 + ε)-approximations to the k-segment and (k, m)-segment mean of a d-dimensional discrete-time signal. The k-segment mean is a k-piecewise linear function that minimizes the regression distance to the signal. The (k,m)-segment mean has an additional constraint that the projection of the k segments on Rd consists of only m ≤ k segments. Existing algorithms for these problems take O(kn2) and nO(mk) time respectively and O(kn2) space, where n is the length of the signal.



Our main tool is a new coreset for discrete-time signals. The coreset is a smart compression of the input signal that allows computation of a (1 + ε)-approximation to the k-segment or (k,m)-segment mean in O(n log n) time for arbitrary constants ε,k, and m. We use coresets to obtain a parallel algorithm that scans the signal in one pass, using space and update time per point that is polynomial in log n. We provide empirical evaluations of the quality of our coreset and experimental results that show how our coreset boosts both inefficient optimal algorithms and existing heuristics. We demonstrate our results for extracting signals from GPS traces. However, the results are more general and applicable to other types of sensors.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{sung2012trajectory,

title = {Trajectory clustering for motion prediction},

author = {Cynthia Sung and Dan Feldman and Daniela Rus},

doi = {10.1109/IROS.2012.6386017},

year  = {2012},

date = {2012-10-07},

booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

pages = {1547-1552},

publisher = {IEEE},

abstract = {We investigate a data-driven approach to robotic path planning and analyze its performance in the context of interception tasks. Trajectories of moving objects often contain repeated patterns of motion, and learning those patterns can yield interception paths that succeed more often. We therefore propose an original trajectory clustering algorithm for extracting motion patterns from trajectory data and demonstrate its effectiveness over the more common clustering approach of using k-means. We use the results to build a Hidden Markov Model of a target's motion and predict movement. Our simulations show that these predictions lead to more effective interception. The results of this work have potential applications in coordination of multi-robot systems, tracking and surveillance tasks, and dynamic obstacle avoidance.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}