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Finding an Optimal Trajectory for Autonomous Parking Under Uncertain Conditions
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Path planning that considers accurate vehicle dynamics and obstacle avoidance is an important problem in the area of autonomous driving. This paper describes a method of implementing trajectory planning for autonomous parking in conditions where the starting point and the position of fixed obstacles are uncertain. The narrow spaces and complicated manoeuvres required for parking demands a lot from the trajectory planning algorithm. It needs to have the ability to accurately model vehicle dynamics and find an efficient way around obstacles. Having obstacles in the way of the parking vehicle makes this a nonconvex problem the goal can usually not be reached by travelling in a straight line and finding a perfect trajectory around them is generally not computationally tractable. This paper reviews a two tiered approach to solving this problem. First a rough path is found using a modified Rapidly-exploring Random Tree (RRT) algorithm called Forward-Backward RRT, which runs two treebuilding processes in parallel and constructs a feasible path from where they intersect. Using optimisation this is then improved into a trajectory that is at least a local optimum. These methods will be demonstrated to produce efficient and feasible trajectories that respects the dynamic constraints of the vehicle and avoids collisions.

Place, publisher, year, edition, pages
2019. , p. 11
Series
TRITA-EECS-EX ; 2019:118
Keywords [en]
RRT, autonomous parking, trajectory planning, optimisation, warm start, heuristics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-254217OAI: oai:DiVA.org:kth-254217DiVA, id: diva2:1329154
Subject / course
Electrical Engineering
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf