Formation Control of Multiple Unmanned Surface Vehicles Using the Adaptive Null-Space-Based Behavioral Method

Abstract

This paper presents an adaptive null-space-based behavioral (NSB) method to deal with the problems of saturation planning and lack of adaptability when the traditional NSB method is applied to the formation control of multiple unmanned surface vehicles (MUSVs). First, the NSB method is analyzed, and the matrix theory is introduced to propose a behavior priority theory determination method based on a vector graph. Second, consider the maneuverability of the unmanned surface vehicle (USV), variable coefficients with physical significances are introduced to redefine the behavioral motion model, making the speed limit solved in each working condition within the maneuvering range of the USV and effectively improving the formation ability of MUSVs. Third, a logical priority collision avoidance strategy between the MUSVs is proposed, aiming at the problem that when the USVs judge each other as obstacles, both of them adopt the obstacle avoidance behavior resulting in two vehicles' courses deviating from the direction of the target point. Finally, a simulation platform for the formation control of MUSVs was established by taking the Dolphin-I prototype USV as the experimental object, and the feasibility of the proposed method was verified by a simulation test. INDEX TERMS Multiple unmanned surface vehicles (MUSVs), adaptability formation control, null-spacebased behavior (NSB), behavior priority, cooperative collision avoidance.

Region-Searching of Multiple Autonomous Underwater Vehicles: A Distributed Cooperative Path-Maneuvering Control Approach

“…Theorem 1. Consider the dynamics (9) and the control laws (25) with (26). Then, error signals u ei , r ei , ψ ei , x ei , and y ei asymptotically converge to zero.…”

“…Theorem 3. Consider a cluster of AUVs with dynamics (4), the control laws (25), and the consensus protocol (42). Then, all error signals in the closed-loop system are GAS.…”

“…In this context, the cooperative path-maneuvering control (CPMC) approaches of multiple vehicles have been significantly developed, such as the leader follower [19,20], the consensus [21,22], and others [23,24]. Obviously, the cooperative system stimulates agents to collaborate with their neighbors and thus completes the assigned mission, which can provide higher efficiency and effectiveness than the individual system [25][26][27]. By virtue of information interactions, the cooperative path-maneuvering is expected to drive multi-vehicles along predesigned paths with a predefined formation configuration [28,29].…”

Distributed Time-Varying Output Formation Tracking Control for General Linear Multi-Agent Systems With Multiple Leaders and Relative Output-Feedback

“…The formation reference is determined by the convex combination of leader k (k ∈ {6, 7, 8}). From Definition 2, the formation reference can be represented by 8 k=6 α k y k (t). The reference output trajectory of follower i (i ∈ {1, 2, ..., 5}) is determined by the formation reference and the desired TVOF vector f i (t), i.e., 8 k=6 α k y k (t) + f i (t).…”

Artificial potential field-based swarm finding of the unmanned surface vehicles in the dynamic ocean environment

“…One of the key issues in the cooperation of multiple USVs is the formation control. Nowadays, there are three dominant approaches being employed to realize the formation control of USVs, including the behavior-based method, [7][8][9] the virtual structure approach, 10,11 and the leader-follower method. [12][13][14][15][16] The fundamental idea of the behavior-based method is to specify various behaviors for all members in the formation and to keep the desired formation by performing prescribed behaviors.…”