A Behavior-Driven Coordination Control Framework for Target Hunting by UUV Intelligent Swarm

Abstract

One of most primitive problems by unmanned underwater vehicle intelligent swarm (UIS) is coordination control, which has a great significance for realization of target hunting with great performance of efficiency and robustness. Existing studies concentrate on behavior-based centralized or distributed control approaches with the prior knowledge and mostly do not elaborately consider behavior conflicts and constraint differences. Therefore, a novel behavior-driven coordination control framework including topology architecture and swarm control which is inspired by immune mechanism, is investigated for target hunting of heterogeneous UIS under unknown and uncertain environment in this paper. For topology architecture, a hybrid non-central distributed topology is developed as a novel immune-inspired architecture to regulate agents with self-organizational and fault-tolerance features. For swarm control, a dual-layer switching control scheme composed by global control and local control, is proposed to drive behaviors via behavioral-intensity, the trigger of switching is when the target is detected. The global control approach is employed to search target, in which two constraints of energy consumption and healthy-state are considered to achieve good operational reliability. While the local control approach is developed to form the dynamic alliance of tracking and capturing, in which behavioral-intensity control strategy for behavior aggregation and decision-making control strategy for behavior selection are respectively designed to avoid behavior conflicts. Simulation results demonstrate that proposed framework can accomplish hunting under various situations such as hunter agent is random or fixed distribution, and the number of targets asynchronously appears. It is confirmed that our framework is capable of achieving the target hunting under unknown and uncertain environment with greater efficiency and robustness. INDEX TERMS Target hunting, behavior-driven, coordination control, hybrid non-central topology, dual-layer switching control.

A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

“…Otherwise, the control will be inaccurate because of the control input and state variables exceed the limits, and this situation will inevitably reduce the tracking performance of the control system [38]. erefore, saturation constraint (10) to limit the control input and hard constraint (11) to limit the state variable are added as follows:…”

“…e multiple mobile robots also enhance the fault tolerance and robustness of the system and strengthen the ability of robots' environment recognition. Comparing with the leader-follower method [5][6][7][8], behavior-based method [9][10][11],and virtual structure method [12,13], the model predictive control (MPC) has attracted attention because of its ability of improving the robustness of the system and having better dynamic control performance in solving the problem of consensus protocol in distributed formation control of multiple mobile robots. Also, MPC is convenient to establish the system model.…”

Algebraic Connectivity Control in Distributed Networks by Using Multiple Communication Channels

“…Increasing the performance of a decentralized multi-agent system has been intensively studied in the last decade, where significant progress has been made in the field of distributed control algorithms including algorithms for swarming [ 7 , 8 , 9 ], consensus [ 10 , 11 , 12 ], and rendezvous [ 13 ]. The ability of multi-agent systems to execute a certain task in parallel has been widely exploited in many applications, including logistics in autonomous warehouses [ 14 ], the exploration of unknown environments [ 15 ], manipulation [ 16 , 17 ], surveillance [ 18 , 19 ], search-and-rescue [ 20 ], interaction with animal species [ 21 , 22 ], and sensor networks [ 23 , 24 , 25 ].…”

Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment

“…The solid grid and arrows in the center of the security domain represent the possible position and direction of movement of the target in the short term. Each green solid dot on the boundary is a certain expected hunting point [33]. 1 2 3 1 3 1 2 1 2 3 2 1 2 3 2 3 1 3 2 3 1 3 1 (3)…”