Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions

Wei, Heng; Lv, Qiang; Duo, Nanxun; Wang, Guosheng; Li, Bing

doi:10.3390/app9051004

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…Peng et al [18] convert the formation control problem to an information consensus problem for nonholonomic robots for converging to a desired formation and maintaining it, assuming that the dynamics of the systems is not entirely known to all robots. Wei et al [19] examine consensus in second-order multi-robot systems under communication delays and noisy measurements, and prove that if the maximum time delay of all robots is bounded, consensus can be maintained. Wang et al [20] examine the problem of consensus in leader-follower systems in systems suffering from time-delayed, noisy communication, showing mean square consensus conditions by several formation control schemes.…”

Section: Consensus-based Formation Controlmentioning

confidence: 99%

Recent Advances in Formations of Multiple Robots

Cohen

Agmon

2021

Curr Robot Rep

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Purpose of Review Formation control is a canonical problem in multi-robot systems, which focuses on the ability of a group of robots to travel in coordination through an area, while maintaining a certain shape or a particular behavior. The robot groups vary in their communication, computation, and sensing capabilities. Moreover, the formation control task itself may have various objectives. These divergences force the use of different models for controlling the formation and for analyzing the task performance. In this paper, we describe the formation control problem and survey recent advances focusing on aspects of maintaining a formation by a group of robots distinguished by the means of analysis.Recent Findings Various approaches may be applied for the sake of formation maintenance, whereas each approach possesses a different perspective in regard with formation control. Recent research focuses on combining those approaches, due to their applicability regarding certain scenarios. For instance, consensus-based control and collision avoidance are usually intertwined together for the sake of reaching a consensus in a manner which is collision-free. Furthermore, machine learning (ML)-based methods for navigating a robot team through unknown complex environments can be incorporated, where the robot team aims to reach a goal position while avoiding collisions and maintaining connectivity. Moreover, recent approaches focus on developing new mechanisms or adapt existing ones for formation control for tolerating limitations in sensing, communication, and coordination, preferably distributively while providing performance guarantees. ConclusionSuch combined approaches yield that the means of analysis, which can be applied to each one separately, can also be utilized in an intertwined manner, and thus provide us with novel methods for preserving formation. Whereas some approaches were vastly investigated (e.g., consensus-based formation control) and need to be adapted to distributed imperfect settings, others still require further insight for unveiling brand new architectures and tools (e.g., ML-based formation control).

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Section: Consensus-based Formation Controlmentioning

confidence: 99%

Recent Advances in Formations of Multiple Robots

Cohen

Agmon

2021

Curr Robot Rep

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“…In [12], an MAS is used in the context of multi-robot formation: first, a distributed consensus algorithm is simulated on a multi-agent based simulation software to assess desired properties despite uncertainty of data and delay in communications, then such algorithm is implemented as an MAS and deployed on a real robotic platform comprising four mobile robots, further assessing effectiveness. In this work, the value added of the MAS lies in its natural predisposition to distribution and tight coupling with environment sensing and actuation, which are necessary features of multi-robot systems.…”

Section: Mas As Execution Infrastructurementioning

confidence: 99%

Special Issue “Multi-Agent Systems”: Editorial

Mariani

Omicini

2020

Applied Sciences

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Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on the environment. The relevance of MAS is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across many different application scenarios and business domains, ranging from logistics to social sciences, from robotics to supply chain, and more. The reason behind such a widespread and diverse adoption lies in MAS great expressive power in modeling and actually supporting operational execution of a variety of systems demanding decentralized computations, reasoning skills, and adaptiveness to change, which are a perfect fit for MAS central notions introduced above. This special issue gathers 11 contributions sampling the many diverse advancements that are currently ongoing in the MAS field.

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“…Over the past decade, a multitude of consensus control algorithms for multi-robot systems (MRS) have emerged, showcasing their effectiveness across diverse domains, such as agriculture, search and rescue, industry, autonomous vehicles, and power grids [1][2][3][4][5][6]. The primary goal of MRS consensus control is to facilitate the convergence of all robots toward a desired state (such as a specific position or heading) while engaging robots in dedicated or shared tasks, including tracking, monitoring, capturing, enclosing, and lifting [7,8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things

Nasir,

Maiti

2024

Machines

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In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique for multi-robot systems that lack prior knowledge of Byzantine attack. Later, an attack observer is proposed to estimate the performance of multi-robot systems in the presence of a Byzantine attack. Additionally, chattering effects are mitigated by employing integral sliding mode control. As a result, resilient consensus performance of multi-robot systems can be achieved in a finite time interval. A simulation example is also presented to validate the effectiveness of the proposed model. Furthermore, we delve into the data structure of the proposed method and explore its integration with Artificial Intelligence for seamless incorporation into the Industrial Internet of Things applications.

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Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions

Cited by 17 publications

References 19 publications

Recent Advances in Formations of Multiple Robots

Recent Advances in Formations of Multiple Robots

Special Issue “Multi-Agent Systems”: Editorial

Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things

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