Abstract
Drones are increasingly being used to perform risky and labor intensive aerial tasks cheaply and safely. To ensure operating costs are low and flights autonomous, their flight plans must be pre-built. In existing techniques drone flight paths are not automatically pre-calculated based on drone capabilities and terrain information. Instead, they focus on adaptive shortest paths, manually determined paths, navigation through camera, images and/or GPS for guidance and genetic or geometric algorithms to guide the drone during flight, all of which makes flight navigation complex and risky. In this paper we present details of an automated flight plan builder DIMPL that pre-builds flight plans for drones tasked with surveying a large area to take photographs of electric poles to identify ones with hazardous vegetation overgrowth. The flight plans are built for subregions allowing the drones to navigate autonomously. DIMPL employs a distributed in-memory paradigm to process subregions in parallel and build flight paths in a highly efficient manner. Experiments performed with network and elevation datasets validated the efficiency of DIMPL in building optimal flight plans for a fleet of different types of drones and demonstrated the tremendous performance improvements possible using the distributed in-memory paradigm.
Highlights
As use of drones rapidly expands, it is aided by improvements in technology such as high speed cameras, sensors, and processors able to analyze the data rapidly and efficiently on the drone
In “Distribution techniques” section, we present an overview of the disk based distribution framework utilized in DIstributed Flight Path buiLder (DIFPL) implementation and the in-memory implementation DIMPL, followed by a description of the experiments conducted in “Experiments” section
The Spark and MapReduce experiments were run on Amazon Web Services (AWS) using Elastic MapReduce clusters
Summary
As use of drones rapidly expands, it is aided by improvements in technology such as high speed cameras, sensors, and processors able to analyze the data rapidly and efficiently on the drone. Better scalability in processing image, terrain, weather and surface data and using it to aid in navigation has allowed them to become increasingly autonomous during flight. Drones have proven very useful in both military battlefield and civilian tasks. Along with common civilian tasks in education [30], studying natural phenomena [45], reconnaissance [36] and conservation [28] they have been used increasingly in surveying farms [41], forests [10] and borders [27]. Autonomous flight presents challenges in terrain navigation, as a multitude of flight path scenarios such as variations in altitude and the density of objects to be surveyed must be taken into account.
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