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Agile collision avoidance for unmanned surface vehicles based on collision shielded model prediction control algorithm

Published online by Cambridge University Press:  18 July 2022

Yihan Tao Open the ORCID record for Yihan Tao [Opens in a new window]  and
Jialu Du Open the ORCID record for Jialu Du [Opens in a new window]
Yihan Tao
Affiliation:
School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Jialu Du*
Affiliation:
School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

Collision avoidance (COLAV) is a prerequisite for the navigation safety of unmanned surface vehicles (USVs). Since USVs have to avoid obstacles clearly and timely, i.e. the COLAV should be agile, the COLAV algorithm should have low computation complexity and make efficient COLAV decisions. However, balancing between the computation complexity and the COLAV decision optimality is still intractable at present. This paper innovatively proposes a COLAV algorithm for USVs by combining the velocity obstacle method with the predictive model method, named the collision shielded model predictive control (CS-MPC) algorithm, such that the agility of USVs COLAV is improved. The runtime of the proposed COLAV algorithm is shortened by shielding the dangerous parts of the search space of the COLAV decisions, and the COLAV decision is efficient with the aid of the accurately predicted motion trajectory by the motion mathematical model of USVs. As such, the USV can safely navigate in complex water areas where multiple vessels and obstacles exist. A series of simulations on a yacht in different kinds of encounter situations were carried out to verify the effectiveness and the agility of the proposed CS-MPC COLAV algorithm.

Keywords

Unmanned surface vehicleautonomousship collision
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 5 , September 2022 , pp. 1243 - 1267
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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