104,506 research outputs found
Efficient exploration of unknown indoor environments using a team of mobile robots
Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels
Video Data Visualization System: Semantic Classification And Personalization
We present in this paper an intelligent video data visualization tool, based
on semantic classification, for retrieving and exploring a large scale corpus
of videos. Our work is based on semantic classification resulting from semantic
analysis of video. The obtained classes will be projected in the visualization
space. The graph is represented by nodes and edges, the nodes are the keyframes
of video documents and the edges are the relation between documents and the
classes of documents. Finally, we construct the user's profile, based on the
interaction with the system, to render the system more adequate to its
references.Comment: graphic
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Urban traffic scenarios often require a high degree of cooperation between
traffic participants to ensure safety and efficiency. Observing the behavior of
others, humans infer whether or not others are cooperating. This work aims to
extend the capabilities of automated vehicles, enabling them to cooperate
implicitly in heterogeneous environments. Continuous actions allow for
arbitrary trajectories and hence are applicable to a much wider class of
problems than existing cooperative approaches with discrete action spaces.
Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS)
in conjunction with Decoupled-UCT evaluates the action-values of each agent in
a cooperative and decentralized way, respecting the interdependence of actions
among traffic participants. The extension to continuous action spaces is
addressed by incorporating novel MCTS-specific enhancements for efficient
search space exploration. The proposed algorithm is evaluated under different
scenarios, showing that the algorithm is able to achieve effective cooperative
planning and generate solutions egocentric planning fails to identify
Ariadne's Thread - Interactive Navigation in a World of Networked Information
This work-in-progress paper introduces an interface for the interactive
visual exploration of the context of queries using the ArticleFirst database, a
product of OCLC. We describe a workflow which allows the user to browse live
entities associated with 65 million articles. In the on-line interface, each
query leads to a specific network representation of the most prevailing
entities: topics (words), authors, journals and Dewey decimal classes linked to
the set of terms in the query. This network represents the context of a query.
Each of the network nodes is clickable: by clicking through, a user traverses a
large space of articles along dimensions of authors, journals, Dewey classes
and words simultaneously. We present different use cases of such an interface.
This paper provides a link between the quest for maps of science and on-going
debates in HCI about the use of interactive information visualisation to
empower users in their search.Comment: CHI'15 Extended Abstracts, April 18-23, 2015, Seoul, Republic of
Korea. ACM 978-1-4503-3146-3/15/0
Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings
In this paper we present a novel interactive multimodal learning system,
which facilitates search and exploration in large networks of social multimedia
users. It allows the analyst to identify and select users of interest, and to
find similar users in an interactive learning setting. Our approach is based on
novel multimodal representations of users, words and concepts, which we
simultaneously learn by deploying a general-purpose neural embedding model. We
show these representations to be useful not only for categorizing users, but
also for automatically generating user and community profiles. Inspired by
traditional summarization approaches, we create the profiles by selecting
diverse and representative content from all available modalities, i.e. the
text, image and user modality. The usefulness of the approach is evaluated
using artificial actors, which simulate user behavior in a relevance feedback
scenario. Multiple experiments were conducted in order to evaluate the quality
of our multimodal representations, to compare different embedding strategies,
and to determine the importance of different modalities. We demonstrate the
capabilities of the proposed approach on two different multimedia collections
originating from the violent online extremism forum Stormfront and the
microblogging platform Twitter, which are particularly interesting due to the
high semantic level of the discussions they feature
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