14 research outputs found
Efficient Multi-Robot Coverage of a Known Environment
This paper addresses the complete area coverage problem of a known
environment by multiple-robots. Complete area coverage is the problem of moving
an end-effector over all available space while avoiding existing obstacles. In
such tasks, using multiple robots can increase the efficiency of the area
coverage in terms of minimizing the operational time and increase the
robustness in the face of robot attrition. Unfortunately, the problem of
finding an optimal solution for such an area coverage problem with multiple
robots is known to be NP-complete. In this paper we present two approximation
heuristics for solving the multi-robot coverage problem. The first solution
presented is a direct extension of an efficient single robot area coverage
algorithm, based on an exact cellular decomposition. The second algorithm is a
greedy approach that divides the area into equal regions and applies an
efficient single-robot coverage algorithm to each region. We present
experimental results for two algorithms. Results indicate that our approaches
provide good coverage distribution between robots and minimize the workload per
robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), 201
OysterNet: Enhanced Oyster Detection Using Simulation
Oysters play a pivotal role in the bay living ecosystem and are considered
the living filters for the ocean. In recent years, oyster reefs have undergone
major devastation caused by commercial over-harvesting, requiring preservation
to maintain ecological balance. The foundation of this preservation is to
estimate the oyster density which requires accurate oyster detection. However,
systems for accurate oyster detection require large datasets obtaining which is
an expensive and labor-intensive task in underwater environments. To this end,
we present a novel method to mathematically model oysters and render images of
oysters in simulation to boost the detection performance with minimal real
data. Utilizing our synthetic data along with real data for oyster detection,
we obtain up to 35.1% boost in performance as compared to using only real data
with our OysterNet network. We also improve the state-of-the-art by 12.7%. This
shows that using underlying geometrical properties of objects can help to
enhance recognition task accuracy on limited datasets successfully and we hope
more researchers adopt such a strategy for hard-to-obtain datasets
Whale Detection Enhancement through Synthetic Satellite Images
With a number of marine populations in rapid decline, collecting and
analyzing data about marine populations has become increasingly important to
develop effective conservation policies for a wide range of marine animals,
including whales. Modern computer vision algorithms allow us to detect whales
in images in a wide range of domains, further speeding up and enhancing the
monitoring process. However, these algorithms heavily rely on large training
datasets, which are challenging and time-consuming to collect particularly in
marine or aquatic environments. Recent advances in AI however have made it
possible to synthetically create datasets for training machine learning
algorithms, thus enabling new solutions that were not possible before. In this
work, we present a solution - SeaDroneSim2 benchmark suite, which addresses
this challenge by generating aerial, and satellite synthetic image datasets to
improve the detection of whales and reduce the effort required for training
data collection. We show that we can achieve a 15% performance boost on whale
detection compared to using the real data alone for training, by augmenting a
10% real data. We open source both the code of the simulation platform
SeaDroneSim2 and the dataset generated through it
An Autonomous Surface Vehicle for Long Term Operations
Environmental monitoring of marine environments presents several challenges:
the harshness of the environment, the often remote location, and most
importantly, the vast area it covers. Manual operations are time consuming,
often dangerous, and labor intensive. Operations from oceanographic vessels are
costly and limited to open seas and generally deeper bodies of water. In
addition, with lake, river, and ocean shoreline being a finite resource,
waterfront property presents an ever increasing valued commodity, requiring
exploration and continued monitoring of remote waterways. In order to
efficiently explore and monitor currently known marine environments as well as
reach and explore remote areas of interest, we present a design of an
autonomous surface vehicle (ASV) with the power to cover large areas, the
payload capacity to carry sufficient power and sensor equipment, and enough
fuel to remain on task for extended periods. An analysis of the design and a
discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto
OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring
Oysters are the living vacuum cleaners of the oceans. There is an exponential
decline in the oyster population due to over-harvesting. With the current
development of the automation and AI, robots are becoming an integral part of
the environmental monitoring process that can be also utilized for oyster reef
preservation. Nevertheless, the underwater environment poses many difficulties,
both from the practical - dangerous and time consuming operations, and the
technical perspectives - distorted perception and unreliable navigation. To
this end, we present a simulated environment that can be used to improve oyster
reef monitoring. The simulated environment can be used to create
photo-realistic image datasets with multiple sensor data and ground truth
location of a remotely operated vehicle(ROV). Currently, there are no
photo-realistic image datasets for oyster reef monitoring. Thus, we want to
provide a new benchmark suite to the underwater community