5,844 research outputs found
Navite: A Neural Network System For Sensory-Based Robot Navigation
A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.Defense Advanced Research Projects Agency (AFOSR 90-0083); Office of Naval Research (N00014-92-J-l309); Consejo Nacional de Ciencia y TecnologĂa (63l462
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple
modalities to a new subspace optimized for one-class classification. The
proposed method iteratively transforms the data from the original feature space
of each modality to a new common feature space along with finding a joint
compact description of data coming from all the modalities. For data in each
modality, we define a separate transformation to map the data from the
corresponding feature space to the new optimized subspace by exploiting the
available information from the class of interest only. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary
material (27 tables, 10 figures). The manuscript and supplementary material
are combined as a single .pdf (50 pages) fil
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
A Study on Learning Social Robot Navigation with Multimodal Perception
Autonomous mobile robots need to perceive the environments with their onboard
sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation
decisions. In order to navigate human-inhabited public spaces, such a
navigation task becomes more than only obstacle avoidance, but also requires
considering surrounding humans and their intentions to somewhat change the
navigation behavior in response to the underlying social norms, i.e., being
socially compliant. Machine learning methods are shown to be effective in
capturing those complex and subtle social interactions in a data-driven manner,
without explicitly hand-crafting simplified models or cost functions.
Considering multiple available sensor modalities and the efficiency of learning
methods, this paper presents a comprehensive study on learning social robot
navigation with multimodal perception using a large-scale real-world dataset.
The study investigates social robot navigation decision making on both the
global and local planning levels and contrasts unimodal and multimodal learning
against a set of classical navigation approaches in different social scenarios,
while also analyzing the training and generalizability performance from the
learning perspective. We also conduct a human study on how learning with
multimodal perception affects the perceived social compliance. The results show
that multimodal learning has a clear advantage over unimodal learning in both
dataset and human studies. We open-source our code for the community's future
use to study multimodal perception for learning social robot navigation
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