Optimization Based Motion Planning With Obstacles And Priorities

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Annan matematik
  • Robotteknik och automation

Nyckelord

Originalspråkengelska
Sidor (från-till)11670-11676
Antal sidor7
TidskriftIFAC-PapersOnLine
Volym50
Utgåva nummer1
StatusPublished - 2017 okt 18
PublikationskategoriForskning
Peer review utfördJa
Evenemang20th IFAC World Congress, 2017 - Toulouse, Frankrike
Varaktighet: 2017 jul 92017 jul 14
https://www.ifac2017.org/

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