Autonomous Parking Using Previous Paths

Siedentop, Christoph; Laukart, Viktor; Krastev, Boris; Kasper, Dietmar; Wedel, Andreas; Breuel, Gabi; Stachniss, Cyrill

doi:10.1007/978-3-319-20855-8_1

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Planning has typically been treated as a less important procedure than control execution [11][12][13][14][15][16], for the following reasons: (i) precise path/trajectory planning is computationally expensive, and (ii) fast path/trajectory planning is rough, but it can be compensated for at the closed-loop execution stage.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization

Zhang

Shao

2016

Knowledge-Based Systems

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization

Zhang

Shao

2016

Knowledge-Based Systems

View full text Add to dashboard Cite

Simulated Sensor Based Strategies for Obstacle Avoidance Using Velocity Profiling for Autonomous Vehicle FURBOT

Masood¹,

Molfino²,

Zoppi³

2020

Electronics

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Freight Urban Robotic Vehicle (FURBOT) is an autonomous vehicle designed to transport last mile freight to designated urban stations. It is a slow vehicle designed to tackle urban environment with complete autonomy. A slow vehicle may have slightly different strategies for avoiding obstacles. Unlike on a highway, it has to deal with pedestrians, traffic lights and slower vehicles while maintaining smoothness in its drive. To tackle obstacle avoidance for this vehicle, sensor feedback based strategies have been formulated for smooth drive and obstacle avoidance. A full mathematical model for the vehicle is formulated and simulated in MATLAB environment. The mathematical model uses velocity control for obstacle avoidance without steering control. The obstacle avoidance is attained through velocity control and strategies are formulated with velocity profiling. Innovative techniques are formulated in creating the simulated sensory feed-backs of the environment. Using these feed-backs, correct velocity profiling is autonomously created for giving velocity profile input to the velocity controller. Proximity measurements are assumed to be available for the vehicle in its given range of drive. Novelty is attained by manipulating velocity profile without prior knowledge of the environment. Four different type of obstacles are modeled for simulated environment of the vehicle. These obstacles are randomly placed in the path of the vehicle and autonomous velocity profiling is verified in simulated environment. The simulated results obtained show satisfactory velocity profiling for controller input. The current technique helps to tune the existing controller and in designing of a better velocity controller for the autonomous vehicle and bridges the gap between sensor feed-back and controller input. Moreover, accurate input profiling creates less strain on the system and brings smoothness in drive for an overall safer environment.

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Autonomous Parking Using Previous Paths

Cited by 2 publications

References 15 publications

Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization

Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization

Simulated Sensor Based Strategies for Obstacle Avoidance Using Velocity Profiling for Autonomous Vehicle FURBOT

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