Server based control flocking for aerial-systems

Oweis, Sami; Ganesan, Subramaniam; Cheok, Ka C.

doi:10.1109/eit.2014.6871783

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…This work may be applied to many problems like running away from heat sources in a case of a fire, or a natural gas leak source (Oweis, S., Ganesan, S. & Cheok, K., 2014;Ganesan, S., Aqeel,A., Shakya, M., & Nambiar, L., 2012;Wikipedia, n.d.;Spector, L., Klein, J., Perry, C., & Feinstein, M., 2003). Another could be in terms of birds getting closer to a school of fish.…”

Section: Discussionmentioning

confidence: 99%

“…Flocking behavior in a virtual environment was first demonstrated by Craig Reynolds, in 1986(Reynolds, C., 1987Oweis, S., Ganesan, S. & Cheok, K., 2014;Wikipedia, n.d.;Red3d.com, n.d.). Reynolds developed a set of simple adaptive rules that each boid could apply to its current position, speed, and orientation.…”

Section: Reynolds Rules Of Flockingmentioning

confidence: 99%

“…Flocking is a behavior that shows a set of independent, self-propelled entities moving together collectively (Reynolds, C., 1987;Oweis, S., Ganesan, S. & Cheok, K., 2014;Wikipedia, n.d.); these entities are called boids (Red3d. com,n.d.).…”

Section: Introductionmentioning

confidence: 99%

“…In other words, each bird has to decide for itself which flock to consider as its environment. Usually, environment is defined as a circle (2D) or an orb (3D) with a particular radius, representing the wing reach or domain of an individual bird (Reynolds, C., 1987;Oweis, S., Ganesan, S. & Cheok, K., 2014;Wikipedia, n.d.;Red3d. com, n.d.).…”

Section: Introductionmentioning

confidence: 99%

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Illustration of Centralized Command and Control for Flocking Behavior

Oweis

Ganesan

Cheok

2014

International Journal of Handheld Computing Research

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Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles' avoiding rule was utilized to ensure that the flock didn't collide with obstacles in each of the boids' paths.

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Section: Discussionmentioning

confidence: 99%

Section: Reynolds Rules Of Flockingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Illustration of Centralized Command and Control for Flocking Behavior

Oweis

Ganesan

Cheok

2014

International Journal of Handheld Computing Research

Self Cite

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“…Adaptive control mechanism is used to trace the peaks of unknown fields for a multi-agent system under uncertain environments in [12]. A server-based approach is employed to control a group of embedded control systems in [13]. Another distributed control scheme is developed by combining consensus algorithms and attraction/repulsion potential functions in [14].…”

Section: Introductionmentioning

confidence: 99%

A Policy Iteration Approach for Flock Motion Control

Abouheaf

Gueaieb

et al. 2021

2021 IEEE International Symposium on Robotic and Sensors Environments (ROSE)

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The flocking motion control is concerned with managing the possible conflicts between local and team objectives of multi-agent systems. The overall control process guides the agents while monitoring the flock-cohesiveness and localization. The underlying mechanisms may degrade due to overlooking the unmodeled uncertainties associated with the flock dynamics and formation. On another side, the efficiencies of the various control designs rely on how quickly they can adapt to different dynamic situations in real-time. An online model-free policy iteration mechanism is developed here to guide a flock of agents to follow an independent command generator over a time-varying graph topology. The strength of connectivity between any two agents or the graph edge weight is decided using a position adjacency dependent function. An online recursive least squares approach is adopted to tune the guidance strategies without knowing the dynamics of the agents or those of the command generator. It is compared with another reinforcement learning approach from the literature which is based on a value iteration technique. The simulation results of the policy iteration mechanism revealed fast learning and convergence behaviors with less computational effort.

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Flocking control of Amigobots with Newton's method

Cheng

Wang

2017

2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Server based control flocking for aerial-systems

Cited by 5 publications

References 5 publications

Illustration of Centralized Command and Control for Flocking Behavior

Illustration of Centralized Command and Control for Flocking Behavior

A Policy Iteration Approach for Flock Motion Control

Flocking control of Amigobots with Newton's method

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