University of Illinois Urbana-Champaign

Bio-inspired vision-based evasion control: collision avoidance without distance measurement

Marinho, Thiago

Content Files
MARINHO-DISSERTATION-2019.pdf

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https://hdl.handle.net/2142/105788

Description

Title
Bio-inspired vision-based evasion control: collision avoidance without distance measurement
Author(s)
Marinho, Thiago
Issue Date
2019-07-08
Director of Research (if dissertation) or Advisor (if thesis)
Hovakimyan, Naira
Doctoral Committee Chair(s)
Hovakimyan, Naira
Committee Member(s)
  • Voulgaris, Petros
  • Stipanovic, Dusan
  • Salapaka, Srinivasa
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2019-11-26T20:49:21Z
Keyword(s)
Collision Avoidance, Evasion Control, Autonomous Systems.
Abstract
In the past decade, the appearance of multiple autonomous vehicle platforms - such as self-driving cars (SDC), unmanned aerial vehicles (UAVs), delivery robots and precision agriculture drones - have emerged with an accelerated pace. Their development has been driven by enormous research at the intersection of control technology and machine learning, enabling autonomous operations, and minimizing human intervention. While the minimization of human intervention has the objective of minimizing the impact of potential human errors, it comes at the price of rigorously formulating and solving challenging problems as collision avoidance that humans quite often do subconsciously and with ease. This dissertation introduces a framework for collision avoidance, where the measurement of the distance to objects and obstacles is not available. This limitation is common to all low-cost and small, ground, or flying vehicles that are not equipped with expensive cameras. The proposed solution takes inspiration from biological systems and the mechanisms that invertebrates and birds use to evade predators. Psychological evidence shows that animals are capable of evading eminent collisions without using depth information, relying instead on looming stimuli. In contrast, the field of robotics has solved collision avoidance among uncooperative vehicles by using depth (the relative distance) to the obstacles as feedback, measured e.g. by lidar, which can be very expensive. To bridge this gap, this works presents a different paradigm in the sensor measurements required for collision avoidance. Relying solely on information that can be directly acquired from a monocular camera this dissertation outlines three control strategies suitable for unicycle-like vehicles avoiding a single, unknown, dynamic uncooperative obstacle: (i) using a line-of-sight (LOS) only measurement, (ii) using a LOS measurement and time-to-collision, and (iii) a LOS, LOS rate and time-to-collision based algorithm. These quantities can readily be estimated from a monocular camera vision system on board the vehicle. Under reasonable assumptions theoretical guarantees are obtained that ensure collision avoidance with an uncooperative moving obstacle.
Graduation Semester
2019-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/105788
Copyright and License Information
Copyright 2019 Thiago Marinho

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