Distributed control of mobile robots in an environment with static obstacles

Giannousakis, Konstantinos; Tzes, Anthony

doi:10.1049/csy2.12018

Cited by 4 publications

(7 citation statements)

References 33 publications

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“…As described in Proposition 2 in Appendix A-B, the convergence of the proposed algorithm is achieved under specific conditions. We also compared the performance of αCVT with other recent coverage control methods presented in [25], [26] and [31]. The algorithm in [25] and [31] only concerned nonconvex environments with obstacles.…”

Section: ) Simulation Resultsmentioning

confidence: 99%

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Adaptive Centroidal Voronoi Tessellation With Agent Dropout and Reinsertion for Multi-Agent Non-Convex Area Coverage

Lee,

Lee

2024

IEEE Access

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Voronoi diagrams are widely used for area partitioning and coverage control. However, their application in non-convex domains typically requires additional computational procedures, such as the application of diffeomorphism, geodesic distance calculations, or incorporation of local markers. These techniques tend to be challenging to extend across diverse non-convex domains. This paper introduces the adaptive centroidal Voronoi tessellation (αCVT) algorithm, integrating iterative centroidal Voronoi tessellation (iCVT) with a novel agent dropout and reinsertion strategy to enhance area coverage control in non-convex domains. This approach demonstrates versatility across varied environments without requiring complex computational processes. The algorithm's efficacy is validated through a series of simulations encompassing non-convex domains with disjoint target areas, obstacles, and shape constraints for both homogeneous and heterogeneous agents. Additionally, the αCVT algorithm is extended for real-time coverage control scenarios. Performance metrics are employed to gauge the distribution of partitioned Voronoi regions and the overall coverage of the target areas, and the results show improved performance compared to the method without adopting the agent dropout and reinsertion strategy.INDEX TERMS Area partitioning, non-convex area coverage control, multi-agent system, Voronoi tessellation.

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Section: ) Simulation Resultsmentioning

confidence: 99%

“…We also compared the performance of αCVT with other recent coverage control methods presented in [25], [26] and [31]. The algorithm in [25] and [31] only concerned nonconvex environments with obstacles. The other [26] performs the coverage and rendezvous controls in the non-convex environment with obstacles.…”

Section: ) Simulation Resultsmentioning

confidence: 99%

Adaptive Centroidal Voronoi Tessellation With Agent Dropout and Reinsertion for Multi-Agent Non-Convex Area Coverage

Lee,

Lee

2024

IEEE Access

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“…Each robot was supported by an omnidirectional range of sensors of a standard radius. The swarm robots are required to move to particular locations [101]. As a result, better algorithms for robot distribution were derived for the problem of area coverage and homing.…”

Section: Applications For Swarm Robotics To Perform Area Coverage Tasksmentioning

confidence: 99%

“…Applying a visibility-based strategy with strong coordination and minimal centralization, executed on AmigoBot hardware in a known environment, solves non-convex area coverage challenges. While the algorithm performs well when trained on local information, it has difficulty when applied to large regions [101]. Using mobile bot hardware, the application explores weak coordination and centralization in static and dynamic environments, with a focus on swarm robot management systems in hospitals during the COVID-19 pandemic.…”

Section: Applications For Swarm Robotics To Perform Area Coverage Tasksmentioning

confidence: 99%

A Survey on Swarm Robotics for Area Coverage Problem

Muhsen,

Sadiq,

Raheem

2023

Algorithms

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The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

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“…The advancement of formation control for autonomous vehicles has accelerated in recent years, see Ref. [3][4][5][6][7][8][9][10][11][12] and references therein. Nevertheless, handling a team in an efficient and robust manner faces new challenges compared to handling a single robot.…”

Section: Introductionmentioning

confidence: 99%

Distributed non‐ideal leader estimation and formation control for multiple non‐holonomic mobile robots

Ren

et al. 2022

IET Cyber-Syst and Robotics

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This paper studies a distributed formation problem for non-holonomic mobile robots. Consideration of the leader dynamics of the robots as non-ideal, that is, subject to disturbances/unmodelled variables, is the distinguishing feature of this work. The issue is resolved by a distributed combined disturbance-and-leader estimator, allowing for the distributed reconstruction of the leader's signals. The estimator needs to detect the leader's information and disturbance. In order to reject such disturbance and achieve the formation asymptotically, the control law incorporates the smooth estimator's estimate of the leader disturbance. Furthermore, the stability of the total distributed formation control algorithm is also examined using the Lyapunov technique. Finally, to show the viability of the proposed theoretical results, simulations and actual experiments are carried out.

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Distributed control of mobile robots in an environment with static obstacles

Cited by 4 publications

References 33 publications

Adaptive Centroidal Voronoi Tessellation With Agent Dropout and Reinsertion for Multi-Agent Non-Convex Area Coverage

Adaptive Centroidal Voronoi Tessellation With Agent Dropout and Reinsertion for Multi-Agent Non-Convex Area Coverage

A Survey on Swarm Robotics for Area Coverage Problem

Distributed non‐ideal leader estimation and formation control for multiple non‐holonomic mobile robots

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