Risk-Based Triggering of Bio-inspired Self-preservation to Protect Robots from Threats

Sing-Kai Chiu, Dejanira Araiza Illan, Kerstin I Eder

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

Safety in autonomous systems has been mostly studied from a human-centered perspective. Besides the loads they may carry, autonomous systems are also valuable property, and self-preservation mechanisms are needed to protect them in the presence of external threats, including malicious robots and antagonistic humans. We present a biologically inspired risk-based triggering mechanism to initiate self-preservation strategies. This mechanism considers environmental and internal system factors to measure the overall risk at any moment in time, to decide whether behaviours such as fleeing or hiding are necessary, or whether the system should continue on its task.We integrated our risk-based triggering mechanism into a delivery rover that is being attacked by a drone and evaluated its effectiveness through systematic testing in a simulated environment in Robot Operating System (ROS) and Gazebo, with a variety of different randomly generated conditions. We compared the use of the triggering mechanism and different configurations of self-preservation behaviours to not having any of these. Our results show that triggering self-preservation increases the distance between the drone and the rover for many of these configurations, and, in some instances, the drone does not catch up with the rover. Our study demonstrates the benefits of embedding risk awareness and self-preservation into autonomous systems to increase their robustness, and the value of using bio-inspired engineering to find solutions in this area.
Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems
Subtitle of host publication18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings
PublisherSpringer
Pages166-181
Number of pages16
ISBN (Electronic)9783319641072
ISBN (Print)9783319641065
DOIs
Publication statusPublished - 20 Jul 2017

Publication series

NameLecture Notes in Computer Science (including Lecture Notes in Artificial Intelligence)
PublisherSpringer
Volume10454
ISSN (Print)0302-9743

Keywords

  • Risk-based triggering mechanism
  • Bio-inspired self-preservation
  • ROS

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