Abstract
Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the individual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with individual applications, for example: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.
Highlights
Unmanned Aerial Systems (UAS) are being increasingly used in a number of applications including surveillance, mapping, defense, search and rescue, mineral exploration, disaster management, assisted guidance and navigation etc
Let us first consider a case of two UASs in a network, both of which are equipped with GNSS and MEMS INS sensor as well as ranging and communication sensors
The error in position continues to increase with time when GNSS signal is unavailable but the rate of increase of error is less in the case when information is shared between the two UASs as compared to the case when there is no sharing of information
Summary
Unmanned Aerial Systems (UAS) are being increasingly used in a number of applications including surveillance, mapping, defense, search and rescue, mineral exploration, disaster management, assisted guidance and navigation etc. To effectively use UAS or swarms of UAS in any of the applications, determining the correct position and orientation of the UAS (or all the UAS in the network) is of utmost importance At present it is achieved by installing GNSS receivers along with some inertial sensors and processing the data collected by sensors using an appropriate filter such as Kalman Filter. Practical challenges include: (i) Payloadpower conundrum, i.e. more power is required to carry more payload and one needs a bigger battery to have more power which in turn increases the payload. This is limiting in the sense that not many sensors can be installed on a single UAS, (ii) Failure of one UAS results in mission failure which is not true in a network of UAS. Further as the size of the UAS becomes smaller, its payload capacity decreases and high accuracy sensors cannot be installed due to their heavy weights resulting in poor positioning accuracy
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