A decentralized algorithm to adaptive trajectory planning for a group of nonholonomic mobile robots

Gil-Pinto, A.; Fraisse, Philippe; Zapata, René

doi:10.1109/iros.2006.281879

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…Three other motion constrains are calculated like Eqs. (10), (11) and (12). Now, for finding the equations of motion based on Kane dynamic procedure as it was discussed about the ramp surface equations; it is required to find the angular velocity that it is in Appendix 2 until by using it; we can calculate the angular momentum motion and also its differential.…”

Section: Equations Of Motion In Cylindrical Surfacementioning

confidence: 99%

“…Two other constrain equations would be like Eqs. 11and (12). Angular velocity of each component is shown in Appendix 2 that using it we can calculate angular momentum.…”

Section: Equations Of Motion In Spherical Surfacementioning

confidence: 99%

“…Therefore, the control algorithm for each robot was defined such that they follow the leader at a certain relative distance and angle [12]. In this survey, follow robot moves in an optimized direction toward the leader robot.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear dynamics and control of a set of robots for hunting and coverage missions

Salarieh

Sabet

2014

Int. J. Dynam. Control

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The aim of this article is control of a set of 3wheel robots with non-holonomic dynamics for hunting and covering around moving target. Robots have mass and inertia, and the wheel mass have been considered in the dynamic model of robots. The output of the controller is wheel's torque and steering torque of the front wheel. The saturation and filtering effects of the actuators are also considered here. The robots in the group are controlled in such a way that each robot responds with an appropriate reaction, based on the control algorithm and the information passed down from other robots and the target (decentralized control). Moving target dynamics has been considered in a way that it is escaping from invader, and the target has holonomics dynamic, and it is assumed that the moving target hasn't the wheel. To derive the equation of the motion, Kane dynamics procedure has been used. Robots are equipped with sensors for distance assessment, vision angle assessment and also signal receiver antenna. To estimate relative position and variables of another robot situation and target, Extended Kalman filter and Extended Kalman smoother (Extended Rauch-Tung-Strible smoother) had been used. To do a group maneuver using inertia analysis and optimizing the norm of error between desired and actual acceleration, controller was designed. The operation was implemented for three planes: ramp plane, spherical plane and cylindrical plane. The results include the hunting and the coverage of the target by four invader robot and relative distance diagrams between robots and the target, their velocity and corresponding to robots at three planes and also a comparison between real and estimated variables and moreover,comparison between estimations by Kalman filter and Kalman smoother. Based on the results,

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Section: Equations Of Motion In Cylindrical Surfacementioning

confidence: 99%

“…Two other constrain equations would be like Eqs. 11and (12). Angular velocity of each component is shown in Appendix 2 that using it we can calculate angular momentum.…”

Section: Equations Of Motion In Spherical Surfacementioning

confidence: 99%

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Nonlinear dynamics and control of a set of robots for hunting and coverage missions

Salarieh

Sabet

2014

Int. J. Dynam. Control

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“…A major issue is whether the group architecture is centralized [29,30] or decentralized [5,23], i.e., whether there is only one control agent, or not. In the second case each robot is autonomous and there is neither a centralized component, nor any other global coordination needed.…”

Section: Introductionmentioning

confidence: 99%

Classifying the multi robot path finding problem into a quadratic competitive complexity class

Sarid

Shapiro

2008

Ann Math Artif Intell

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We explore two motion planning problems where a group of mobile robots has to reach a target located in an a priori unknown environment while on-line planning the next step. In the first problem the target position is unknown and should be found by the robots, while in the second problem the target position is known and only a path to it should be found. We focus on optimizing the cost of the task in terms of motion time, which, under the assumption of uniform velocity of all the robots, correlates to the path length passed by the robot which reaches the target. The performance of an on-line algorithm is usually expressed in terms of Competitiveness, the constant ratio between the on-line and the optimal off-line solutions. Specifically, the ratio between the lengths of the actual path made by the robot which reached the target to the shortest path to the target. We use generalized competitiveness, i.e., the ratio is not necessarily constant, but could be any function. Classification of a motion planning task in the sense of performance is done by finding an upper and a lower bounds on the competitiveness of all algorithms solving that task. If the two bounds belong to the same functional class this is the Competitive Complexity Class of the task. We find the two bounds for the aforementioned common on-line motion planning problems, and classify them into competitive classes. It is shown that in general any on-line motion planning algorithm that tries to solve these problems must have at least a quadratic competitive performance. This is a lower bound of 170 S. Sarid, A. Shapiro the problems. This paper describes two new on-line navigation algorithm which solve the problems under discussion. The first is called MRSAM, short for MultiRobot Search Area Multiplication, and the second is called MRBUG, short for MultiRobot BUG which extends Lumelsky famous BUG algorithm. Both algorithms have quadratic upper bounds, which prove that the problems they solve have quadratic upper bounds. Thus it is shown that navigation in an unknown environment by a group of robots belongs to a quadratic competitive class. MRSAM and MRBUG have a quadratic competitive performance and thus have optimal competitiveness. The algorithms' performance is simulated in office-like environments.

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“…2.1 Técnica Virtual Force File (VFF) (modificada de [10] [19]). b) Representação 3D da ZVD para o robô manipulador (retirada de [20] O trabalho desenvolvido nesta dissertação abrange o campo da robótica móvel, que é considerada como uma área de pesquisa que lida com o controle de veículos autônomos ou semi-autônomos [1].…”

Section: Lista De Figurasunclassified

Zona virtual deformável com filtro de partículas no rastreamento de obstáculos em robótica móvel

Chavez¹,

Robert²

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Dedicado esta dissertação aos meus pais Blanca Nubia Chavez e Dumer Muñoz, à minha esposa Clara Milena Concha. AGRADECIMENTOS A Deus, por ter me dado força, paciência, inteligência e ensinado o caminho certo. Aos meus pais Blanca Nubia Chavez e Dumer Muñoz, ao meu irmão Javier Andres Muñoz, pela confiança e apoio. À minha adorada esposa Clara Milena Concha, que sempre me apoiou na longa realização deste trabalho. À professora Carla Koike, pela oportunidade, e orientação no desenvolvimento deste trabalho. Ao professor Flávio Vidal, por contribuições. Aos meus amigos pelo entusiasmo nos momentos difíceis.

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A decentralized algorithm to adaptive trajectory planning for a group of nonholonomic mobile robots

Cited by 7 publications

References 6 publications

Nonlinear dynamics and control of a set of robots for hunting and coverage missions

Nonlinear dynamics and control of a set of robots for hunting and coverage missions

Classifying the multi robot path finding problem into a quadratic competitive complexity class

Zona virtual deformável com filtro de partículas no rastreamento de obstáculos em robótica móvel

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