KEYWORDS: Sensors, Energy harvesting, Solar energy, Clouds, Energy efficiency, Wind energy, Detection and tracking algorithms, Fusion energy, Vegetation, Surveillance
This paper considers the exploitation of energy harvesting technologies for teams of Autonomous Vehicles (AVs).
Traditionally, the optimisation of information gathering tasks such as searching for and tracking new objects, and
platform level power management, are only integrated at a mission-management level. In order to truly exploit new
energy harvesting technologies which are emerging in both the commercial and military domains (for example the
'EATR' robot and next-generation solar panels), the sensor management and power management processes must be
directly coupled. This paper presents a novel non-myopic sensor management framework which addresses this issue
through the use of a predictive platform energy model. Energy harvesting opportunities are modelled using a dynamic
spatial-temporal energy map and sensor and platform actions are optimised according to global team utility. The
framework allows the assessment of a variety of different energy harvesting technologies and perceptive tasks. In this
paper, two representative scenarios are used to parameterise the model with specific efficiency and energy abundance
figures. Simulation results indicate that the integration of intelligent power management with traditional sensor
management processes can significantly increase operational endurance and, in some cases, simultaneously improve
surveillance or tracking performance. Furthermore, the framework is used to assess the potential impact of energy
harvesting technologies at various efficiency levels. This provides important insight into the potential benefits that
intelligent power management can offer in relation to improving system performance and reducing the dependency on
fossil fuels and logistical support.
Three-dimensional (3D) imaging technologies have considerable potential for aiding military operations in areas such as
reconnaissance, mission planning and situational awareness through improved visualisation and user-interaction. This
paper describes the development of fast 3D imaging capabilities from low-cost, passive sensors. The two systems
discussed here are capable of passive depth perception and recovering 3D structure from a single electro-optic sensor
attached to an aerial vehicle that is, for example, circling a target. Based on this example, the proposed method has been
shown to produce high quality results when positional data of the sensor is known, and also in the more challenging case
when the sensor geometry must be estimated from the input imagery alone. The methods described exploit prior
knowledge concerning the type of sensor that is used to produce a more robust output.
The overall goal of the research project reported here is to create a novel system that can combine input from multiple
passive sensors at different viewpoints (such as uninhabited aerial vehicles) into a single integrated three-dimensional
(3D) view of a scene. This form of intelligent data processing, known as Volume Registration, can further exploit the
available information to enable improved surveillance, reconnaissance and situational awareness, and thus offers
substantial potential benefit to military applications. This paper focuses on the case of multiple sensors onboard UAVs
operating at mid-altitude, and describes two complementary techniques that have been investigated in parallel to address
this challenge. The first of these is depth from disparity, which allows a real-time per-pixel estimation of the distance of
scene objects from the camera; the second is shape from silhouette, which back-projects a segmented version of the
image onto a 3D block of voxels and 'carves' a 3D model over multiple frames. The main steps of each algorithm are
outlined, along with appropriate results, in order to demonstrate how they could form a useful part of a practical Volume
Registration system. A number of possible extensions and improvements to the system architecture are also discussed to
improve the accuracy and efficiency of these techniques, and their applicability to the more complex low-altitude case is
discussed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.