7,905 research outputs found
Reverse k Nearest Neighbor Search over Trajectories
GPS enables mobile devices to continuously provide new opportunities to
improve our daily lives. For example, the data collected in applications
created by Uber or Public Transport Authorities can be used to plan
transportation routes, estimate capacities, and proactively identify low
coverage areas. In this paper, we study a new kind of query-Reverse k Nearest
Neighbor Search over Trajectories (RkNNT), which can be used for route planning
and capacity estimation. Given a set of existing routes DR, a set of passenger
transitions DT, and a query route Q, a RkNNT query returns all transitions that
take Q as one of its k nearest travel routes. To solve the problem, we first
develop an index to handle dynamic trajectory updates, so that the most
up-to-date transition data are available for answering a RkNNT query. Then we
introduce a filter refinement framework for processing RkNNT queries using the
proposed indexes. Next, we show how to use RkNNT to solve the optimal route
planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route
from a start location to an end location that could attract the maximum (or
minimum) number of passengers based on a pre-defined travel distance threshold.
Experiments on real datasets demonstrate the efficiency and scalability of our
approaches. To the best of our best knowledge, this is the first work to study
the RkNNT problem for route planning.Comment: 12 page
Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
The young infant explores its body, its sensorimotor system, and the
immediately accessible parts of its environment, over the course of a few
months creating a model of peripersonal space useful for reaching and grasping
objects around it. Drawing on constraints from the empirical literature on
infant behavior, we present a preliminary computational model of this learning
process, implemented and evaluated on a physical robot. The learning agent
explores the relationship between the configuration space of the arm, sensing
joint angles through proprioception, and its visual perceptions of the hand and
grippers. The resulting knowledge is represented as the peripersonal space
(PPS) graph, where nodes represent states of the arm, edges represent safe
movements, and paths represent safe trajectories from one pose to another. In
our model, the learning process is driven by intrinsic motivation. When
repeatedly performing an action, the agent learns the typical result, but also
detects unusual outcomes, and is motivated to learn how to make those unusual
results reliable. Arm motions typically leave the static background unchanged,
but occasionally bump an object, changing its static position. The reach action
is learned as a reliable way to bump and move an object in the environment.
Similarly, once a reliable reach action is learned, it typically makes a
quasi-static change in the environment, moving an object from one static
position to another. The unusual outcome is that the object is accidentally
grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically
with the hand. Learning to make grasps reliable is more complex than for
reaches, but we demonstrate significant progress. Our current results are steps
toward autonomous sensorimotor learning of motion, reaching, and grasping in
peripersonal space, based on unguided exploration and intrinsic motivation.Comment: 35 pages, 13 figure
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