Dynamic Leader Allocation in Multi-robot Systems Based on Nonlinear Model Predictive Control

Tavares, Augusto de Holanda Barreto Martins; Madruga, Sarah Pontes; Brito, Alisson V.; Nascimento, Tiago P.

doi:10.1007/s10846-019-01064-4

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…This will be indispensable for further research especially in view of engineering background and intelligence paradigm of swarm robotics. 8,[11][12][13] FUNDING This work is supported by the National Science Foundation of China (Nos. 51875331 and 11672169).…”

Section: Resultsmentioning

confidence: 99%

“…[7][8][9][10] Recently, some researchers devoted themselves to developing different kinds of consensus or synchronization tracking control approaches for MNWMRs from various perspectives, including neural network optimization (NNO), nonlinear model predictive control (NMPC), and Takagi-Sugeno type fuzzy automaton (TS-TFA). [11][12][13] Generally speaking, on the one hand, the tracking control or stabilization of NWMR is a challenging research issue. This is mainly because a single wheeled mobile robot in a plane under the nonholonomic constraints has three degree-of-freedoms (DOFs), yet it is controlled using two control inputs only.…”

Section: Introductionmentioning

confidence: 99%

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Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Liu

Guo

et al. 2020

Intl J Robust & Nonlinear

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This paper proposes a sliding-mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position-tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single-robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method. K E Y W O R D Smulti-nonholonomic mobile robots, polar coordinates, practical consensus tracking, sliding-mode control 1 Int J Robust Nonlinear Control. 2020;30:3831-3847.wileyonlinelibrary.com/journal/rnc

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Liu

Guo

et al. 2020

Intl J Robust & Nonlinear

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show abstract

“…However, these methods have limited effectiveness in accurately tracking the target formation [18], which restricts configuration changes to variations in time, scales, and orientation of the target configuration [19][20][21]. From a control perspective, the dynamic change of configuration can be specified at the global formation level [19][20][21][22] or the local level [23,24]. The global approach involves presetting the path points of the formation's center of gravity for agents to follow, maintaining the rigid body structure by preserving relative positions and transitioning between different formation shapes.…”

Section: Introductionmentioning

confidence: 99%

Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System

Kang,

Xu,

Bian

2024

Applied Sciences

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The use of affine maneuver control to maintain the desired configuration of unmanned aerial vehicle (UAV) swarms has been widely practiced. Nevertheless, the lack of capability to interact with obstacles and navigate autonomously could potentially limit its extension. To address this problem, we present an innovative formation flight system featuring a virtual leader that seamlessly integrates global control and local control, effectively addressing the limitations of existing methods that rely on fixed configuration changes to accommodate real-world constraints. To enhance the elasticity of an algorithm for configuration change in an obstacle-laden environment, this paper introduces a second-order differentiable virtual force-based metric for planning local trajectories. The virtual field comprises several artificial potential field (APF) forces that adaptively adjust the formation compared to the existing following control. Then, a distributed and decoupled trajectory optimization framework that considers obstacle avoidance and dynamic feasibility is designed. This novel multi-agent agreement strategy can efficiently coordinate the global planning and local trajectory optimizations of the formation compared to a single method. Finally, an affine-based maneuver approach is employed to validate an optimal formation control law for ensuring closed-loop system stability. The simulation results demonstrate that the proposed scheme improves track accuracy by 32.92% compared to the traditional method, while also preserving formation and avoiding obstacles simultaneously.

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Formation control of unmanned micro aerial vehicles for straitened environments

et al. 2020

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Dynamic Leader Allocation in Multi-robot Systems Based on Nonlinear Model Predictive Control

Cited by 11 publications

References 46 publications

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System

Formation control of unmanned micro aerial vehicles for straitened environments

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