Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance
In recent years, the advent of new hardware and software technologies for navigation and control has made Unmanned Aerial Vehicles (UAVs) ever more autonomous and efficient. As a consequence, it is now possible to have drones moving within complex environments, such as cities or indoor areas. One of...
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ftutrentoiris:oai:iris.unitn.it:11572/278290 2024-02-11T10:07:43+01:00 Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance Bojeri, Alex Iacca, Giovanni Bojeri, Alex Iacca, Giovanni 2020 http://hdl.handle.net/11572/278290 https://doi.org/10.23919/FRUCT49677.2020.9211037 https://ieeexplore.ieee.org/document/9211037 eng eng IEEE country:USA place:Piscataway, NJ info:eu-repo/semantics/altIdentifier/isbn/978-952-69244-3-4 info:eu-repo/semantics/altIdentifier/wos/WOS:000628527300003 ispartofbook:Proceedings of the 27th Conference of Open Innovations Association FRUCT FRUCT 27 firstpage:18 lastpage:26 numberofpages:9 http://hdl.handle.net/11572/278290 doi:10.23919/FRUCT49677.2020.9211037 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85093655625 https://ieeexplore.ieee.org/document/9211037 info:eu-repo/semantics/closedAccess info:eu-repo/semantics/conferenceObject 2020 ftutrentoiris https://doi.org/10.23919/FRUCT49677.2020.9211037 2024-01-16T23:12:25Z In recent years, the advent of new hardware and software technologies for navigation and control has made Unmanned Aerial Vehicles (UAVs) ever more autonomous and efficient. As a consequence, it is now possible to have drones moving within complex environments, such as cities or indoor areas. One of the main requirements for intelligent mission planning in such environments is the ability to correctly and efficiently detect and avoid obstacles. For this reason, various libraries have been created for the simulation of UAV navigation in virtual environments, in order to test algorithms for automatic obstacle detection and collision avoidance before deploying the drones in the real world. Usually, the performance of these algorithms depends on various parameters as well as specific application settings. However, while different parameter configurations can be easily tested in simulation, their number can be too large to allow a complete exploration of the parameter space or a manual tuning. Furthermore, a full analytical model of the parameters' influence on the algorithmic performance can be hard to obtain. Yet, it is extremely important to find their optimal values to allow collision-free navigation. In this direction, we propose here a thorough exploration, based on an Evolutionary Algorithm (EA), of the parameter space of the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. Our results show that the proposed EA is a viable solution for finding optimal parameter settings that can be generalizable to different scenarios characterized by different complexity levels. Conference Object Orca Università degli Studi di Trento: CINECA IRIS 2020 27th Conference of Open Innovations Association (FRUCT) 18 26 |
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Università degli Studi di Trento: CINECA IRIS |
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English |
description |
In recent years, the advent of new hardware and software technologies for navigation and control has made Unmanned Aerial Vehicles (UAVs) ever more autonomous and efficient. As a consequence, it is now possible to have drones moving within complex environments, such as cities or indoor areas. One of the main requirements for intelligent mission planning in such environments is the ability to correctly and efficiently detect and avoid obstacles. For this reason, various libraries have been created for the simulation of UAV navigation in virtual environments, in order to test algorithms for automatic obstacle detection and collision avoidance before deploying the drones in the real world. Usually, the performance of these algorithms depends on various parameters as well as specific application settings. However, while different parameter configurations can be easily tested in simulation, their number can be too large to allow a complete exploration of the parameter space or a manual tuning. Furthermore, a full analytical model of the parameters' influence on the algorithmic performance can be hard to obtain. Yet, it is extremely important to find their optimal values to allow collision-free navigation. In this direction, we propose here a thorough exploration, based on an Evolutionary Algorithm (EA), of the parameter space of the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. Our results show that the proposed EA is a viable solution for finding optimal parameter settings that can be generalizable to different scenarios characterized by different complexity levels. |
author2 |
Bojeri, Alex Iacca, Giovanni |
format |
Conference Object |
author |
Bojeri, Alex Iacca, Giovanni |
spellingShingle |
Bojeri, Alex Iacca, Giovanni Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance |
author_facet |
Bojeri, Alex Iacca, Giovanni |
author_sort |
Bojeri, Alex |
title |
Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance |
title_short |
Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance |
title_full |
Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance |
title_fullStr |
Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance |
title_full_unstemmed |
Evolutionary optimization of Drone Trajectories Based on Optimal Reciprocal Collision Avoidance |
title_sort |
evolutionary optimization of drone trajectories based on optimal reciprocal collision avoidance |
publisher |
IEEE |
publishDate |
2020 |
url |
http://hdl.handle.net/11572/278290 https://doi.org/10.23919/FRUCT49677.2020.9211037 https://ieeexplore.ieee.org/document/9211037 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
info:eu-repo/semantics/altIdentifier/isbn/978-952-69244-3-4 info:eu-repo/semantics/altIdentifier/wos/WOS:000628527300003 ispartofbook:Proceedings of the 27th Conference of Open Innovations Association FRUCT FRUCT 27 firstpage:18 lastpage:26 numberofpages:9 http://hdl.handle.net/11572/278290 doi:10.23919/FRUCT49677.2020.9211037 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85093655625 https://ieeexplore.ieee.org/document/9211037 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.23919/FRUCT49677.2020.9211037 |
container_title |
2020 27th Conference of Open Innovations Association (FRUCT) |
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18 |
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26 |
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1790606394964049920 |