A distributed robot swarm control for dynamic region coverage

Teruel, Enrique; Aragüés, Rosario; López‐Nicolás, Gonzalo

doi:10.1016/j.robot.2019.06.002

Cited by 24 publications

(8 citation statements)

References 16 publications

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“…The authors of [29] also compared the influence of the agent's geodesic or Euclidean sensing patterns using the geodesic or Euclidean Voronoi partitioning in non-convex environments. Stergiopoulos et al [30] addressed the coverage of a moving deformable convex region and introduced a feedforward action to speed up the Centroidal Voronoi Tessellations process.…”

Section: Related Workmentioning

confidence: 99%

A virtual force interaction scheme for multi-robot environment monitoring

Zhang

Zhi

et al. 2022

Robotics and Autonomous Systems

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Section: Related Workmentioning

confidence: 99%

A virtual force interaction scheme for multi-robot environment monitoring

Zhang

Zhi

et al. 2022

Robotics and Autonomous Systems

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“…A cluster is a composite object in space {𝒳 , 𝒴}. The number of single objects 𝑘 combined in a cluster is one of the components of the set of internal parameters Y (1) = 𝑘.…”

Section: The Formal Side Of the Problemmentioning

confidence: 99%

Boundaries of computational complexity and optimal cluster’s quantity for controlled swarm in non-cooperative games

2021

AND

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The purpose of the work is to determine the relationship between the computational complexity of controlling a swarm of particles and the available computational resources for choosing the optimal control strategy. We find formulas for the relationship between the available computational complexity, the number of clusters in the swarm, the number of interacting players, and the depth of calculations to search the suboptimal control. Methods. To find the optimal control, the method of maximizing the target objective function is used. The computational complexity of the objective function is determined for suboptimal control on the grid for the maximum number of particle swarms and the minimum of the grid size. To study the swarm dynamics in the configuration space we investigate the properties of the convex hull of the swarm using elements of the particle diffusion theory. Results. The objective function of controlling a swarm of particles in the conditions of interaction with stationary objects and in the presence of competitive swarms is constructed. It is shown that the swarm of particles spread in the configuration space due to instability and mixing in the general situation. Formulas for the maximum possible size of clusters and the number of particles in clusters, the depth of calculation of control steps and the detail of the grid are obtained. The connection between the dynamics of the swarm of controlled particles and the theory of Smolukhowski's coagulation in colloidal solutions is shown. Conclusion. Constraints on the computational complexity of the control lead to a restriction on the size of the iteration grid for finding the minimax and to a restriction on the number of swarm clusters for which the optimal strategy can be chosen. The clustering capability of the swarm leads to the fact that the product of the number of positions in the grid in the optimization of the number of clusters in a swarm depth calculation steps should be no more than the order of the logarithm of acceptable computational complexity.

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“…The algorithm introduces a limited range of sub-divisions to limit the amount of communication between robots, and the convergence of the algorithm is verified in simulation. In [16], an adaptive control algorithm is designed to drive the robot to the weighted centroid of the corresponding Voronoi cell, and finally the Lyapunov function is used to prove that the robot network can converge to the optimal configuration.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

et al. 2022

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In view of the low formation redundancy in the traditional rigid formation algorithm and its difficulty in dynamically adapting to the external environment, this study considers the use of the CVT (centroidal Voronoi tessellation) algorithm to control multiple robots to form the desired formation. This method significantly increases the complexity of the multi-robot system, its structural redundancy, and its internal carrying capacity. First, we used the CVT algorithm to complete the Voronoi division of the global map, and then changed the centroid position of the Voronoi cell by adjusting the density function. When the algorithm converged, it could ensure that the position of the generated point was the centroid of each Voronoi cell and control the robot to track the position of the generated point to form the desired formation. The use of traditional formations requires less consideration of the impact of the actual environment on the health of robots, the overall mission performance of the formation, and the future reliability. We propose a health optimization management algorithm based on minor changes to the original framework to minimize the health loss of robots and reduce the impact of environmental restrictions on formation sites, thereby improving the robustness of the formation system. Simulation and robot formation experiments proved that the CVT algorithm could control the robots to quickly generate formations, easily switch formations dynamically, and solve the formation maintenance problem in obstacle scenarios. Furthermore, the health optimization management algorithm could maximize the life of unhealthy robots, making the formation more robust when performing tasks in different scenarios.

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A distributed robot swarm control for dynamic region coverage

Cited by 24 publications

References 16 publications

A virtual force interaction scheme for multi-robot environment monitoring

A virtual force interaction scheme for multi-robot environment monitoring

Boundaries of computational complexity and optimal cluster’s quantity for controlled swarm in non-cooperative games

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

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