Hierarchical decomposed-objective model predictive control for autonomous casualty extraction
File(s)
Published version
Author(s)
Saputra, Roni Permana
Rakicevic, Nemanja
Chappell, Digby
Wang, Ke
Kormushev, Petar
Type
Journal Article
Abstract
In recent years, several robots have been developed and deployed to perform casualty extraction tasks. However, the majority of these robots are overly complex, and require teleoperation via either a skilled operator or a specialised device, and often the operator must be present at the scene to navigate safely around the casualty. Instead, improving the autonomy of such robots can reduce the reliance on expert operators and potentially unstable communication systems, while still extracting the casualty in a safe manner. There are several stages in the casualty extraction procedure, from navigating to the location of the emergency, safely approaching and loading the casualty, to finally navigating back to the medical assistance location. In this paper, we propose a Hierarchical Decomposed-Objective based Model Predictive Control (HiDO-MPC) method for safely approaching and manoeuvring around the casualty. We implement this controller on ResQbot — a proof-of-concept mobile rescue robot we previously developed — capable of safely rescuing an injured person lying on the ground, i.e. performing the casualty extraction procedure. HiDO-MPC achieves the desired casualty extraction behaviour by decomposing the main objective into multiple sub-objectives with a hierarchical structure. At every time step, the controller evaluates this hierarchical decomposed objective and generates the optimal control decision. We have conducted a number of experiments both in simulation and using the real robot to evaluate the proposed method’s performance, and compare it with baseline approaches. The results demonstrate that the proposed control strategy gives significantly better results than baseline approaches in terms of accuracy, robustness, and execution time, when applied to casualty extraction scenarios.
Date Issued
2021-03
Date Acceptance
2021-02-23
Citation
IEEE Access, 2021, 9, pp.39656-39679
ISSN
2169-3536
Publisher
IEEE
Start Page
39656
End Page
39679
Journal / Book Title
IEEE Access
Volume
9
Copyright Statement
© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Identifier
https://ieeexplore.ieee.org/document/9369351
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Robots
Robot sensing systems
Task analysis
Navigation
Rescue robots
Loading
Mobile robots
Autonomous casualty extraction
mobile rescue robot
mobile robot control
model predictive control
search and rescue
08 Information and Computing Sciences
09 Engineering
10 Technology
Publication Status
Published
Date Publish Online
2021-03-04