Collaborative online planning for automated victim search in disaster response

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Title: Collaborative online planning for automated victim search in disaster response
Author(s): Beck, Z
Luke Teacy, WT
Rogers, A
Jennings, NR
Item Type: Journal Article
Abstract: Collaboration is essential for effective performance by groups of robots in disaster response settings. Here we are particularly interested in heterogeneous robots that collaborate in complex scenarios with incomplete, dynamically changing information. In detail, we consider an automated victim search setting, where unmanned aerial vehicles (UAVs) with different capabilities work together to scan for mobile phones and find and provide information about possible victims near these phone locations. The state of the art for such collaboration is robot control based on independent planning for robots with different tasks and typically incorporates uncertainty with only a limited scope. In contrast, in this paper, we take into account complex relations between robots with different tasks. As a result, we create a joint, full-horizon plan for the whole robot team by optimising over the uncertainty of future information gain using an online planner with hindsight optimisation. This joint plan is also used for further optimisation of individual UAV paths based on the long-term plans of all robots. We evaluate our planner’s performance in a realistic simulation environment based on a real disaster and find that our approach finds victims 25% faster compared to current state-of-the-art approaches.
Publication Date: 4-Nov-2017
Date of Acceptance: 1-Nov-2017
ISSN: 0921-8890
Publisher: Elsevier
Start Page: 251
End Page: 266
Journal / Book Title: Robotics and Autonomous Systems
Volume: 100
Copyright Statement: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Keywords: 0801 Artificial Intelligence And Image Processing
0906 Electrical And Electronic Engineering
0913 Mechanical Engineering
Industrial Engineering & Automation
Publication Status: Published
Embargo Date: 2018-11-04
Open Access location:
Appears in Collections:Faculty of Natural Sciences

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