21,926 research outputs found
Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight
This paper tackles the problem of planning minimum-energy coverage paths for
multiple UAVs. The addressed Multi-UAV Coverage Path Planning (mCPP) is a
crucial problem for many UAV applications such as inspection and aerial survey.
However, the typical path-length objective of existing approaches does not
directly minimize the energy consumption, nor allows for constraining energy of
individual paths by the battery capacity. To this end, we propose a novel mCPP
method that uses the optimal flight speed for minimizing energy consumption per
traveled distance and a simple yet precise energy consumption estimation
algorithm that is utilized during the mCPP planning phase. The method
decomposes a given area with boustrophedon decomposition and represents the
mCPP as an instance of Multiple Set Traveling Salesman Problem with a minimum
energy objective and energy consumption constraint. The proposed method is
shown to outperform state-of-the-art methods in terms of computational time and
energy efficiency of produced paths. The experimental results show that the
accuracy of the energy consumption estimation is on average 97% compared to
real flight consumption. The feasibility of the proposed method was verified in
a real-world coverage experiment with two UAVs.Comment: in IEEE Robotics and Automation Letter
Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel
This paper describes the design, implementation and testing of a suite of
algorithms to enable depth constrained autonomous bathymetric (underwater
topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth
and a bounding polygon, the ASV will find and follow the intersection of the
bounding polygon and the depth contour as modeled online with a Gaussian
Process (GP). This intersection, once mapped, will then be used as a boundary
within which a path will be planned for coverage to build a map of the
Bathymetry. Methods for sequential updates to GP's are described allowing
online fitting, prediction and hyper-parameter optimisation on a small embedded
PC. New algorithms are introduced for the partitioning of convex polygons to
allow efficient path planning for coverage. These algorithms are tested both in
simulation and in the field with a small twin hull differential thrust vessel
built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field
Robotic
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
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