Potential-function-based shape formation in swarm simulation

Jung, Hahmin; Kim, Dong Hun

doi:10.1007/s12555-013-0133-6

Cited by 15 publications

(11 citation statements)

References 10 publications

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“…In the potential function, the attractive potential is an attractive force between a robot and a goal point [16]. The attractive potential function can be defined as…”

Section: Potential Fieldmentioning

confidence: 99%

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

Jung

Kim

2020

IJFIS

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This paper proposes the local path planning of a mobile robot using a novel grid-based potential method. The proposed method can be easily applied to a robotic system in a real environment because it is designed based on actual sensors. This method solves the local minimum problems that occur frequently in the potential field. A new repulsive field is created between adjacent obstacles that a mobile robot cannot pass through by calculating the distance between the robot and the obstacles. The generated repulsion field causes a mobile robot to escape from the local minimum. MATLAB simulations are used to compare the proposed and conventional potential field methods.

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“…In the potential function, the attractive potential is an attractive force between a robot and a goal point [16]. The attractive potential function can be defined as…”

Section: Potential Fieldmentioning

confidence: 99%

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

Jung

Kim

2020

IJFIS

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“…GE S S et al [14] introduced the concept of potential trenches, which provided a more flexible and scalable solution to formation control problem. Subsequently, the literatures [15][16][17] combined the concept of artificial potential field to further study the formation control problem. The designed controllers could realize formation regulation control [15,16] or formation tracking control [17] for a variety of simple connected graphs.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the literatures [15][16][17] combined the concept of artificial potential field to further study the formation control problem. The designed controllers could realize formation regulation control [15,16] or formation tracking control [17] for a variety of simple connected graphs. In addition, because the artificial potential field has an accurate function description form, it is beneficial to use the Lyapunov method for stability analysis [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…The designed controllers could realize formation regulation control [15,16] or formation tracking control [17] for a variety of simple connected graphs. In addition, because the artificial potential field has an accurate function description form, it is beneficial to use the Lyapunov method for stability analysis [15][16][17]. In Ref [18], the behaviorbased method has been fully verified for its superiority in handling large-scale swarm formation control problems.…”

Section: Introductionmentioning

confidence: 99%

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Complex Shape Formation Control for Swarms Using Artificial Potentials

Duan

Zhou

Shi³

2021

Lecture Notes in Electrical Engineering

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In this paper, we propose a decentralized control algorithm to solve the problem of shape formation in homogeneous swarms, which enables a swarm of agents to achieve a specific shape formation without collision. At the micro level, the algorithm makes each agent move to the desired shape domain without collision after receiving the shape information. At the macro level, the emergence of orderly formation swarm behavior is realized in this process. The Lyapunov method is used for the stability analysis of the agent swarm system. Simulation results demonstrate that the algorithm has strong flexibility, which can meet the formation requirements of various complex graphs such as convex, non-convex, connected and non-connected graph, and good adaptability to large-scale swarm.

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“…Therefore, complicated formation shape can be achieved by designing appropriate regional boundary conditions, such as circular shape, ring shape, and crescent shape [17]. Jung and Kim proposed a potential-function-based shape formation control law, where a diamond-shaped formation and a heart-shaped formation are achieved [18]. There is no special leader or follower in these region-based formations where the rolls and identities of the robots are the same.…”

Section: Introductionmentioning

confidence: 99%

Adaptive‐Neural‐Network‐Based Shape Control for a Swarm of Robots

Lan

et al. 2018

Complexity

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This paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functions. Moreover, the collision avoidance, velocity consensus, and region tracking are all considered in the controller. The stability of the multirobot system has been demonstrated based on the Lyapunov theorem. Finally, three numerical simulations show the effectiveness of the proposed formation control scheme to deal with the narrow space, loss of robots, and formation merging problems.

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Potential-function-based shape formation in swarm simulation

Cited by 15 publications

References 10 publications

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

Complex Shape Formation Control for Swarms Using Artificial Potentials

Adaptive‐Neural‐Network‐Based Shape Control for a Swarm of Robots

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