Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm
In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favor of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/21/5237 |
| _version_ | 1797631355962523648 |
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| author | Geesara Kulathunga Alexandr Klimchik |
| author_facet | Geesara Kulathunga Alexandr Klimchik |
| author_sort | Geesara Kulathunga |
| collection | DOAJ |
| description | In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favor of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning, both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforementioned target metrics more efficiently compared to other approaches, such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyze related literature in order to understand how the motion planning problem, especially the trajectory planning problem, is formulated when being applied for generating optimal trajectories in real-time for multirotor aerial vehicles, as well as how it impacts the listed metrics. As a result of this research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline. |
| first_indexed | 2024-03-11T11:22:36Z |
| format | Article |
| id | doaj.art-b9d518b4416f4903bb567dd32648216b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T11:22:36Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b9d518b4416f4903bb567dd32648216b2023-11-10T15:11:29ZengMDPI AGRemote Sensing2072-42922023-11-011521523710.3390/rs15215237Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control ParadigmGeesara Kulathunga0Alexandr Klimchik1Institute of Robotics and Computer Vision, Innopolis University, Innopolis 420500, RussiaSchool of Computer Science, Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln LN6 7TS, UKIn general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favor of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning, both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforementioned target metrics more efficiently compared to other approaches, such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyze related literature in order to understand how the motion planning problem, especially the trajectory planning problem, is formulated when being applied for generating optimal trajectories in real-time for multirotor aerial vehicles, as well as how it impacts the listed metrics. As a result of this research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline.https://www.mdpi.com/2072-4292/15/21/5237multirotor aerial vehicles (MAVs)B-splineminimum-snapmodel predictive control (MPC)nonlinear model predictive control (NMPC)linear quadratic regulator (LQR) |
| spellingShingle | Geesara Kulathunga Alexandr Klimchik Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm Remote Sensing multirotor aerial vehicles (MAVs) B-spline minimum-snap model predictive control (MPC) nonlinear model predictive control (NMPC) linear quadratic regulator (LQR) |
| title | Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm |
| title_full | Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm |
| title_fullStr | Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm |
| title_full_unstemmed | Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm |
| title_short | Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm |
| title_sort | survey on motion planning for multirotor aerial vehicles in plan based control paradigm |
| topic | multirotor aerial vehicles (MAVs) B-spline minimum-snap model predictive control (MPC) nonlinear model predictive control (NMPC) linear quadratic regulator (LQR) |
| url | https://www.mdpi.com/2072-4292/15/21/5237 |
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