Experimental validation of multi-vehicle coordination strategies

Marshall, Joshua A.; Fung, T.; Broucke, Mireille E.; D’Eleuterio, Gabriele M. T.; Francis, Bruce A.

doi:10.1109/acc.2005.1470106

Cited by 9 publications

(9 citation statements)

References 4 publications

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“…Although each car is only aware of two others, the swarm operates as a whole because of the iterative coupling utilized by the daisy-chain system. The performance of the swarm is somewhat dependent on initial placement of the micro-cars, however, a behavior noted in similar systems [6]. If the cars are ordered by ID, the daisy chain swarm performs excellently; the cars rarely collide and find a common heading quickly.…”

Section: A Cooperative Motion Algorithmsmentioning

confidence: 95%

A Third Generation Micro-Vehicle Testbed for Cooperative Control and Sensing Strategies

Gonzalez

Huang

Martinez

et al. 2011

Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics

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Abstract-This paper describes the third generation of an economical cooperative control testbed, last described in [K. K. Leung et al., Proc. Am. Cont. Conf., 2007]. The new testbed vehicles are improved with powerful on-board computing, upgraded and expanded on-board sensing, and enhanced wireless communication, while maintaining economic feasibility and scale. The new hardware allows for increased autonomy of each vehicle and for the implementation of new, more advanced algorithms that rely on increased computational capability. We demonstrate practical use of the testbed for algorithm validation by implementing both previous and new cooperative steering and barrier avoidance algorithms.

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Section: A Cooperative Motion Algorithmsmentioning

confidence: 95%

A Third Generation Micro-Vehicle Testbed for Cooperative Control and Sensing Strategies

Gonzalez

Huang

Martinez

et al. 2011

Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics

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show abstract

“…and hence from Lemma 2, the following BMI thus to the inequality (20) Therefore, according to the BMI solution subject to (16) and by considering Lemma 1, the effects of the external disturbance $ i on the regulated output´i are attenuated. By substituting K into (4) and by considering x .n/ i D u i , one can get…”

Section: Proofmentioning

confidence: 99%

Decentralized H∞ consensus protocol for a class of high-order multiagent systems

Rezaee

Abdollahi

2016

Int. J. Robust. Nonlinear Control

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SUMMARYThis paper proposes a consensus protocol for a class of high-order multiagent systems under directed networks. It is supposed that each agent is exposed to an external disturbance additive to its control input. Based on the H 1 optimization theory, the consensus protocol gains are designed in order to attenuate the effects of the external disturbances on the performance of the multiagent system. The main problem of existing H 1 high-order consensus protocols in the literature is the dependency of the design on the information of coupling matrices associated with networks topologies. Despite existing H 1 high-order consensus protocols in the literature, the proposed consensus protocol can be designed in a fully decentralized manner based on no global information. The main idea of the design is to propose an H 1 control formulation in which the coupling information of the agents is considered as exogenous signals, while the coupling effects of these signals lead to achieving consensus in the multiagent system. Numerical examples verify the effectiveness of the proposed consensus protocol.

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“…Depending on the local information, agents gradually update their headings to the same direction. Another experiment achievement introduced in [12] demonstrates a distributed multi-agent cyclic pursuit algorithm which uses the relative angles between neighbours. Both of the [11] and [12] concern the agent headings, and agents forward velocities are defined as constant.…”

Section: Introductionmentioning

confidence: 99%

“…Another experiment achievement introduced in [12] demonstrates a distributed multi-agent cyclic pursuit algorithm which uses the relative angles between neighbours. Both of the [11] and [12] concern the agent headings, and agents forward velocities are defined as constant. Multi-agent system introduced in [13] is capable of controlling both the distances and angles between neighbours by using the local information.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Control of Distributed Flocking System

Wang

2007

2007 International Conference on Mechatronics and Automation

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This paper presents a fuzzy based flocking system. To maintain the distance between agents, we use fuzzy logic controller to design a "force function" which is related to the relative distance between neighbours. And the "force function" is used to control the velocity of agent. To prove the stability of flocking system, we build a Hamilton function which is the kinetic energy of flocking system. Utilizing the LaSalle's invariance principle, we prove that the system is stable. Specially, we developed a local form flocking controller which is derived from the global flocking algorithm. By using the local controller, agents in the flocking system only need to know the local information (relative distances and relative angles between neighbours). To evaluate the performance, we simulate the flocking system with 200 agents under the control of distributed flocking algorithm.Index Terms-Multiple robot, sensor network, distributed system.

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Experimental validation of multi-vehicle coordination strategies

Cited by 9 publications

References 4 publications

A Third Generation Micro-Vehicle Testbed for Cooperative Control and Sensing Strategies

A Third Generation Micro-Vehicle Testbed for Cooperative Control and Sensing Strategies

Decentralized H∞ consensus protocol for a class of high-order multiagent systems

Fuzzy Control of Distributed Flocking System

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