Date of Award
8-2023
Document Type
Masters Thesis
Degree Name
M.S.
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
First Advisor
Christopher Reardon
Second Advisor
Daniel Baack
Third Advisor
Maggie Wigness
Fourth Advisor
Matthew Rutherford
Keywords
Adversary aware navigation, Autonomous robot navigation, Context aware navigation, Preference based learning, Robot learning, Terrain aware navigation
Abstract
In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain types in the environment. These weights are then combined into a costmap, which is passed to a planner for path generation and navigation to a goal.
This thesis extends prior research in learning terrain costs for robot navigation using an active rewards learning Python package, APReL. The novel contribution of this thesis is that the robot not only considers traversability affordances of different terrains for navigation, but also considers concealment from an adversary in the environment. This research work also augments the APReL package with wrappers suitable for the use-case. The model will be evaluated by comparing its performance with an already existing Inverse Optimal Control (IOC) model trained from human demonstrations as a baseline learning algorithm for autonomous robot navigation.
Copyright Date
8-2023
Copyright Statement / License for Reuse
All Rights Reserved.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Aniekan Ufot Inyang
Provenance
Received from ProQuest
File Format
application/pdf
Language
English (eng)
Extent
86 pgs
File Size
7.3 MB
Recommended Citation
Inyang, Aniekan Ufot, "Terrain and Adversary-Aware Autonomous Robot Navigation" (2023). Electronic Theses and Dissertations. 2299.
https://digitalcommons.du.edu/etd/2299
Discipline
Robotics, Artificial intelligence