Optimization Based Motion Planning With Obstacles And Priorities
(2017) 20th IFAC World Congress, 2017 In IFAC-PapersOnLine 50(1). p.11670-11676- Abstract
- The goal of this work is to explore ways of generating state trajectories for dynamical systems subject to computational constraints, obstacles and priority assignment. The algorithms are developed for a miniature unmanned aerial vehicle (UAV) in a modular fashion and include (1) a genetic algorithm (GA) for solving the traveling salesman problem (TSP) with respect to priorities and obstacle avoidance, (2) a projective algorithm (PA) for finding the shortest paths around obstacles, (3) a quadratic program (QP) for minimum-snap polynomial trajectory generation subject to equality constraints to guarantee avoidance of static obstacles. Combined, the algorithms enable simple and computationally efficient motion planning with support in both... (More)
- The goal of this work is to explore ways of generating state trajectories for dynamical systems subject to computational constraints, obstacles and priority assignment. The algorithms are developed for a miniature unmanned aerial vehicle (UAV) in a modular fashion and include (1) a genetic algorithm (GA) for solving the traveling salesman problem (TSP) with respect to priorities and obstacle avoidance, (2) a projective algorithm (PA) for finding the shortest paths around obstacles, (3) a quadratic program (QP) for minimum-snap polynomial trajectory generation subject to equality constraints to guarantee avoidance of static obstacles. Combined, the algorithms enable simple and computationally efficient motion planning with support in both R2 and R3 exemplified in a real-time implementation. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b12d27fd-e3ca-4b72-bbbe-3dc93a3b6cdc
- author
- Greiff, Marcus LU and Robertsson, Anders LU
- organization
- publishing date
- 2017-10-18
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- UAV, motion planning, projective path, heuristic TSP, optimisation
- in
- IFAC-PapersOnLine
- volume
- 50
- issue
- 1
- pages
- 7 pages
- publisher
- IFAC Secretariat
- conference name
- 20th IFAC World Congress, 2017
- conference location
- Toulouse, France
- conference dates
- 2017-07-09 - 2017-07-14
- external identifiers
-
- scopus:85044864885
- ISSN
- 2405-8963
- DOI
- 10.1016/j.ifacol.2017.08.1677
- project
- Semantic Mapping and Visual Navigation for Smart Robots
- language
- English
- LU publication?
- yes
- id
- b12d27fd-e3ca-4b72-bbbe-3dc93a3b6cdc
- date added to LUP
- 2017-11-27 20:44:09
- date last changed
- 2024-04-15 00:02:53
@article{b12d27fd-e3ca-4b72-bbbe-3dc93a3b6cdc,
abstract = {{The goal of this work is to explore ways of generating state trajectories for dynamical systems subject to computational constraints, obstacles and priority assignment. The algorithms are developed for a miniature unmanned aerial vehicle (UAV) in a modular fashion and include (1) a genetic algorithm (GA) for solving the traveling salesman problem (TSP) with respect to priorities and obstacle avoidance, (2) a projective algorithm (PA) for finding the shortest paths around obstacles, (3) a quadratic program (QP) for minimum-snap polynomial trajectory generation subject to equality constraints to guarantee avoidance of static obstacles. Combined, the algorithms enable simple and computationally efficient motion planning with support in both R2 and R3 exemplified in a real-time implementation.}},
author = {{Greiff, Marcus and Robertsson, Anders}},
issn = {{2405-8963}},
keywords = {{UAV; motion planning; projective path; heuristic TSP; optimisation}},
language = {{eng}},
month = {{10}},
number = {{1}},
pages = {{11670--11676}},
publisher = {{IFAC Secretariat}},
series = {{IFAC-PapersOnLine}},
title = {{Optimization Based Motion Planning With Obstacles And Priorities}},
url = {{http://dx.doi.org/10.1016/j.ifacol.2017.08.1677}},
doi = {{10.1016/j.ifacol.2017.08.1677}},
volume = {{50}},
year = {{2017}},
}