Citation: Li, J.; Li, C.; Zhang, H.
Distributed Dynamic Predictive
Control for Multi-AUV Target
Searching and Hunting in Unknown
Environments. Machines 2022, 10, 366.
https://doi.org/10.3390/machines
10050366
Academic Editors: Jacopo Aguzzi,
Giacomo Picardi, Damianos
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Received: 25 March 2022
Accepted: 6 May 2022
Published: 11 May 2022
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Article
Distributed Dynamic Predictive Control for Multi-AUV Target
Searching and Hunting in Unknown Environments
Juan Li , Chengyue Li and Honghan Zhang *
Institute of Ocean Installations and Control Technology, College of Intelligent Systems Science and Engineering,
Harbin Engineering University, Harbin 150001, China; lijuan041@hrbeu.edu.cn (J.L.);
lichengyue@hrbeu.edu.cn (C.L.)
* Correspondence: zhanghonghan2008@163.com
Abstract:
The research and development of the ocean has been gaining in popularity in recent years,
and the problem of target searching and hunting in the unknown marine environment has been a
pressing problem. To solve this problem, a distributed dynamic predictive control (DDPC) algorithm
based on the idea of predictive control is proposed. The task-environment region information and the
input of the AUV state update are obtained by predicting the state of multi-AUV systems and making
online task optimization decisions and then locking the search area for the following moment. Once
a moving target is found in the search process, the AUV conducts a distributed hunt based on the
theory of potential points, which solves the problem of the reasonable distribution of potential points
during the hunting process and realizes the formation of hunting rapidly. Compared with other
methods, the simulation results show that the algorithm exhibits high efficiency and adaptability.
Keywords: multi-AUV; cooperation; distributed dynamic prediction; hunting; target search
1. Introduction
It is always a challenging task to explore and develop the enormous, complex, and
hazardous marine environment, and an autonomous underwater vehicle (AUV) is the
best technical means to deal with the current challenges as it is an underwater device
with good concealment, flexibility in underwater movement, economic applicability, and
other technical characteristics of high-tech devices [
1
]. The reconnaissance efficiency of a
single AUV is low because of realistic conditions such as limited energy consumption and
restricted communication. Therefore, greater multi-AUV collaborative operation is required.
In order to accomplish underwater tasks better, the AUV needs to have capabilities of
adaptive searching, decision-making and dynamic target hunting. Therefore, how to utilize
limited resources rationally and coordinate AUVs to complete target searching and hunting
are the most critical problems.
For the problem of multiple-agent-system target searches in an unknown environment,
extensive research has been carried out [
2
]. Liu et al. establish the distributed multi-AUVs
collaborative search system (DMACSS) and proposed the autonomous collaborative search-
learning algorithm (ACSLA) be integrated into the DMACSS. The test results demonstrate
that the DMACSS runs stably and the search accuracy and efficiency of ACSLA outperform
other search methods, thus better realizing the cooperation between AUVs and allowing
the DMACSS to find the target more accurately and faster [
3
]. A fuzzy-based bio-inspired
neural network approach was proposed by Sun for multi-AUV target searching, which
can effectively plan search paths. Moreover, a fuzzy algorithm was introduced into the
bio-inspired neural network to make the trajectory of AUV obstacle avoidance smoother.
Simulation results show that the proposed algorithm can control a multi-AUV system to
complete multi-target search tasks with higher search efficiency and adaptability [
4
]. For
multiple AUVs, Yao et al. proposed bidirectional negotiation with a biased min-consensus
(BN-BMC) algorithm to determine the allocation and prioritization of sub-regions to be
Machines 2022, 10, 366. https://doi.org/10.3390/machines10050366 https://www.mdpi.com/journal/machines
