3,499 research outputs found
Adaptive Informative Path Planning with Multimodal Sensing
Adaptive Informative Path Planning (AIPP) problems model an agent tasked with
obtaining information subject to resource constraints in unknown, partially
observable environments. Existing work on AIPP has focused on representing
observations about the world as a result of agent movement. We formulate the
more general setting where the agent may choose between different sensors at
the cost of some energy, in addition to traversing the environment to gather
information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS
requires reasoning jointly about the effects of sensing and movement in terms
of both energy expended and information gained. We frame AIPPMS as a Partially
Observable Markov Decision Process (POMDP) and solve it with online planning.
Our approach is based on the Partially Observable Monte Carlo Planning
framework with modifications to ensure constraint feasibility and a heuristic
rollout policy tailored for AIPPMS. We evaluate our method on two domains: a
simulated search-and-rescue scenario and a challenging extension to the classic
RockSample problem. We find that our approach outperforms a classic AIPP
algorithm that is modified for AIPPMS, as well as online planning using a
random rollout policy.Comment: First two authors contributed equally; International Conference on
Automated Planning and Scheduling (ICAPS) 202
Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing
Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers
the problem of an agent equipped with multiple sensors, each with different
sensing accuracy and energy costs. The agent's goal is to explore the
environment and gather information subject to its resource constraints in
unknown, partially observable environments. Previous work has focused on the
less general Adaptive Informative Path Planning (AIPP) problem, which considers
only the effect of the agent's movement on received observations. The AIPPMS
problem adds additional complexity by requiring that the agent reasons jointly
about the effects of sensing and movement while balancing resource constraints
with information objectives. We formulate the AIPPMS problem as a belief Markov
decision process with Gaussian process beliefs and solve it using a sequential
Bayesian optimization approach with online planning. Our approach consistently
outperforms previous AIPPMS solutions by more than doubling the average reward
received in almost every experiment while also reducing the root-mean-square
error in the environment belief by 50%. We completely open-source our
implementation to aid in further development and comparison
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
In this work, a deep learning approach has been developed to carry out road
detection by fusing LIDAR point clouds and camera images. An unstructured and
sparse point cloud is first projected onto the camera image plane and then
upsampled to obtain a set of dense 2D images encoding spatial information.
Several fully convolutional neural networks (FCNs) are then trained to carry
out road detection, either by using data from a single sensor, or by using
three fusion strategies: early, late, and the newly proposed cross fusion.
Whereas in the former two fusion approaches, the integration of multimodal
information is carried out at a predefined depth level, the cross fusion FCN is
designed to directly learn from data where to integrate information; this is
accomplished by using trainable cross connections between the LIDAR and the
camera processing branches.
To further highlight the benefits of using a multimodal system for road
detection, a data set consisting of visually challenging scenes was extracted
from driving sequences of the KITTI raw data set. It was then demonstrated
that, as expected, a purely camera-based FCN severely underperforms on this
data set. A multimodal system, on the other hand, is still able to provide high
accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI
road benchmark where it achieved excellent performance, with a MaxF score of
96.03%, ranking it among the top-performing approaches
Graph-based View Motion Planning for Fruit Detection
Crop monitoring is crucial for maximizing agricultural productivity and
efficiency. However, monitoring large and complex structures such as sweet
pepper plants presents significant challenges, especially due to frequent
occlusions of the fruits. Traditional next-best view planning can lead to
unstructured and inefficient coverage of the crops. To address this, we propose
a novel view motion planner that builds a graph network of viable view poses
and trajectories between nearby poses, thereby considering robot motion
constraints. The planner searches the graphs for view sequences with the
highest accumulated information gain, allowing for efficient pepper plant
monitoring while minimizing occlusions. The generated view poses aim at both
sufficiently covering already detected and discovering new fruits. The graph
and the corresponding best view pose sequence are computed with a limited
horizon and are adaptively updated in fixed time intervals as the system
gathers new information. We demonstrate the effectiveness of our approach
through simulated and real-world experiments using a robotic arm equipped with
an RGB-D camera and mounted on a trolley. As the experimental results show, our
planner produces view pose sequences to systematically cover the crops and
leads to increased fruit coverage when given a limited time in comparison to a
state-of-the-art single next-best view planner.Comment: 7 pages, 10 figures, accepted at IROS 202
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