Real-Time Mission-Motion Planner for Multi-UUVs Cooperative Work Using Tri-Level Programing

Sun, Siqing; Song, Baowei; Wang, Peng; Dong, Huachao; Chen, Xiao

doi:10.1109/tits.2020.3023819

Cited by 19 publications

(7 citation statements)

References 41 publications

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“…To increase the efficiency of robots and autonomous vehicles for these applications, a concept of multiple collaborative robotics or swam robotics have been introduced [3]. For underwater environment, multiple collaborative autonomous underwater vehicles (AUVs) have shown their great efficiency for many challenging applications like seabed monitoring, wind turbine inspection, marine debris monitoring and cleaning, etc [4]. However, controlling multiple AUVs working collaboratively is not a trivial task because the effects of nonlinear dynamics, communication delay between AUVs, and the effects of underwater environmental disturbances, This work was partly supported by the Natural Environment Research Council, United Kingdom [grant number NE/V008080/1] and by the Royal Society [grant number IEC/NSFC/211236 and grant number RGS/R1/221356].…”

Section: Introductionmentioning

confidence: 99%

“…This ensures a global fixed time convergence of the whole collaborative multiple AUVs system. • Unlike the existing consensus formation controllers for multiple AUVs [4], [22], [15], which do no consider the input saturation issues, this paper incorporates an adaptive auxiliary function into the fixed-time distributed consensus controller to handle the problem of saturated control efforts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Fixed-Time Consensus Control for Multiple AUV Systems with Input Saturations

Van¹,

Sun²,

Mcllvanna³

et al. 2023

Preprint

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This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time. First, a formation control protocol based on a fixed-time backstepping sliding mode control is designed, in which the consensus cooperative tracking errors for each AUV will be formulated. Then, to compensate for the saturated control torques, an adaptive auxiliary variable is introduced. Finally, a fixed-time adaptive fuzzy logic control (FLC) is derived to approximate the unknown dynamics, in which the adaptive laws of the FLC is derived such that the adaptive signals and errors can be convergent within a fixed time. The fixed time convergence is desired in practice because it provides an exciting property that the global convergence of the whole system is independent with the initial states of the AUVs. The computer simulation results for a consensus formation control of four AUVs show that the proposed formation control can provide high tracking performance with lower and smoother control efforts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Fixed-Time Consensus Control for Multiple AUV Systems with Input Saturations

Van¹,

Sun²,

Mcllvanna³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [4], a new technology based on receiver operating characteristic (ROC) analysis is used to centrally handle the UUV task allocation problem, and the path planning problem is solved using a distributed model. To address the dynamic UUV task allocation problem, Sun et al design a tri-level optimization method to plan safe paths in severe ocean environments, in which UUVs have limited detection ranges and are required to respond to emergencies in real time [5]. To guarantee the effectiveness of scheme allocation, heuristic algorithms commonly require a certain number of optimization iteration processes to generate, update, and select solutions, leading to high time consumption in dynamic task environments.…”

Section: Introductionmentioning

confidence: 99%

A Period Training Method for Heterogeneous UUV Dynamic Task Allocation

Xie

Yang²,

Gao

et al. 2023

Electronics

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In the dynamic task allocation of unmanned underwater vehicles (UUVs), the schemes of UUVs need to be quickly reallocated to respond to emergencies. The most common heuristic allocation method uses predesigned optimization rules to iteratively obtain a solution, which is time-consuming. To quickly assign tasks to heterogeneous UUVs, we propose a novel task allocation algorithm based on multi-agent reinforcement learning (MARL) and a period training method (PTM). The period training method (PTM) is used to optimize the parameters of MARL models in different training environments, improving the algorithm’s robustness. The simulation results show that the proposed methods can effectively allocate tasks to different UUVs within a few seconds and reallocate the schemes in real time to deal with emergencies.

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“…In centralized methods, agents communicate their situation awareness (SA) to a central planner, which generates a plan for the entire swarm. Sun et al established a three-level model including command level, mission planning level, and motion planning level for multiple UUVs visiting a set of underwater sites in a large-scale and time-varying environment, and implemented a real-time task planning by selecting heuristic algorithms based on the characteristics of the different levels [ 1 ]. This type of methods places more of the heavy processing requirements on the central planner, thus making the agents smaller and cheaper to build with lower cost.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms

Yuan

et al. 2022

Sensors

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Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a “UUV-task” matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.

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Real-Time Mission-Motion Planner for Multi-UUVs Cooperative Work Using Tri-Level Programing

Cited by 19 publications

References 41 publications

Distributed Fixed-Time Consensus Control for Multiple AUV Systems with Input Saturations

Distributed Fixed-Time Consensus Control for Multiple AUV Systems with Input Saturations

A Period Training Method for Heterogeneous UUV Dynamic Task Allocation

A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms

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