Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines

Kim, Dong Hun; Wang, H.O.; Ye, Guohua; Shin, Seiichi

doi:10.1109/cdc.2004.1428623

Cited by 33 publications

(41 citation statements)

References 9 publications

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“…In this section, the problem of adaptive dynamical synchronization is illustrated by two examples with simple models of the leader and the follower systems, frequently used in fields of formation control and self-organization of swarm systems [44,45].…”

Section: Applicationsmentioning

confidence: 99%

Dynamical adaptive synchronization

Efimov

2006

Adaptive Control & Signal

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Section: Applicationsmentioning

confidence: 99%

Dynamical adaptive synchronization

Efimov

2006

Adaptive Control & Signal

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“…Lastly, there may be additional optimality type of requirements. As in [9,10] presented by the authors, we divide the problem into two sub-problems. At the individual robot level, artificial potential function based methods [10] by the author are used to solve the problem of path planning and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

“…As in [9,10] presented by the authors, we divide the problem into two sub-problems. At the individual robot level, artificial potential function based methods [10] by the author are used to solve the problem of path planning and obstacle avoidance. At the group level, new artificial potential based methods are employed to manage the flocking motion as well as enhanced obstacle avoidance in a group setting.…”

Section: Introductionmentioning

confidence: 99%

“…The overall framework explores the benefits of judiciously integrating the robot level and group level controls. In [10], each robot makes self-organization using position information of all neighbors to get successive group behaviors. However, such a scheme takes more data acquisition followed by time consumption and complex communication environments, since it requires fully connected association among all robots.…”

Section: Introductionmentioning

confidence: 99%

“…It is envisioned that the outcomes of swarm research can impact a wide variety of applications such as the deployment of unmanned ground and air vehicles for both military and civil missions, satellite formations, and large-scale cooperative mobile sensor and device networks, to name a few. Though a large number of techniques have been studied in the literature [1][2][3][4][5][6][7][8][9][10][11][12], very few attempts have been made by applying a particle swarm optimization (PSO), one of the evolutionary computation (EC) techniques [13], for self-organization of swarm agents, e.g., the prey-pursuit of ants. PSO has been compared to genetic algorithms for efficiently finding optimal or near-optimal solutions in large search spaces [15,16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self-organization of unicycle swarm robots based on a modified particle swarm framework

Kim

2010

Int. J. Control Autom. Syst.

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In this paper, an attempt has been made to incorporate some special features in the conventional particle swarm optimization (PSO) technique for unicycle robots with line of sight. The modified particle swarm framework (MPSF) for the self-organization of unicycle swarm robots is proposed and studied. The framework explores the benefits by dynamic associations with the proposed MPSF to realize complex swarming behaviors. In this scheme, out of sights by obstacles or neighbor robots are considered when a robot moves to a target. Each robot self-organizes to flock to the best robot in the swarm seen in its sight and migrates to a moving target while avoiding collision with obstacles and neighboring robots through the interaction of robots with line of sight. The resulting MPSF is advantageous for practical implementations since it is used for robots, even in cases where a target is blocked by obstacles or robots. Extensive simulation is presented to illustrate the viability and effectiveness of the proposed framework.

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Flocking coordination using active leader and local information

Etemadi

Alasty

Vossoughi

2011

Asian Journal of Control

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In this paper coordination of a group of agents by a coordinator agent, called the leader-agent (LA), is discussed. Agents have identical finite sensing radii and access only the local information. Inter-agent interactions bring them together as a multi-agent network system. In a recently proposed flocking algorithm using a virtual leader, it is proved that if a fraction of agents are informed then the center of mass of the multi-agent system tracks the virtual leader. The minimum number of the fraction is always greater than one. In this paper the only informed agent is the LA. Interaction functions are defined to make the model of the multi-agent system suitable for the design of the coordination control algorithm. The leader-agent accesses its local data and actively plans its motions through a nonlinear control method. It is proven in this paper that the multi-agent system tracks the virtual leader and a locally defined point near the LA converges to the position of the virtual leader. It is also shown that in this case, center of mass of the multi-agent system follows the virtual leader but with a finite distance. We have also discussed positive effects of the LA on network connectivity. Finally illustrative examples are added as well.

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Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines

Cited by 33 publications

References 9 publications

Dynamical adaptive synchronization

Dynamical adaptive synchronization

Self-organization of unicycle swarm robots based on a modified particle swarm framework

Flocking coordination using active leader and local information

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