Leader–follower formation control of nonholonomic mobile robots with input constraints

Consolini, Luca; Morbidi, Fabio; Prattichizzo, Domenico; Tosques, Mario

doi:10.1016/j.automatica.2007.09.019

Cited by 605 publications

(330 citation statements)

References 11 publications

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“…The distance covered by each robot, total cycles in simulation and the average distance involved are also calculated. In each of the simulation, the goal is assumed to be at coordinates (4,6) and the obstacles are placed at coordinates (2,2) and (3,4) with a radius of 0.5 units. The coordinates of the starting location of the robots are assigned as (1,1) for robot 1, (1.5,1) for robot 2, (1,1.5) for robot 3, (1.2,0.7) for robot 4, (1.25,1.35) for robot 5 and (1.3,0.9) for robot 6.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system

Mathew

2018

MATEC Web Conf.

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Abstract.One of the primary ability of an intelligent mobile robot system is obstacle avoidance. BUG algorithms are classic examples of the algorithms used for achieving obstacle avoidance. Unlike many other planning algorithms based on global knowledge, BUG algorithms assume only local knowledge of the environment and a global goal. Among the variations of the BUG algorithms that prevail, BUG-0, BUG-1 and BUG-2 are the more prominent versions. The exhaustive search algorithm present in BUG-1 makes it more reliable and safer for practical applications. Overall, this provides a more predictable and dependable performance. Hence, the essential focus in this paper is on implementing the BUG-1 algorithm across a group of robots to move them from a start location to a target location. The results are compared with the results from BUG-1 algorithm implemented on a single robot. The strategy developed in this work reduces the time involved in moving the robots from starting location to the target location. Further, the paper shows that the total distance covered by each robot in a multi robot-system is always lesser than or equal to that travelled by a single robot executing the same problem.

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Section: Simulation Resultsmentioning

confidence: 99%

“…The leadership exchange is the backbone of the leader-follower system [6], [7]. This was utilized in the present work to design an efficient multi robot obstacle avoidance strategy.…”

Section: Introductionmentioning

confidence: 99%

Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system

Mathew

2018

MATEC Web Conf.

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“…3(a)]. In this case, system (27) The last equation is of the type A sin(a) + B cos(a) + C = 0 and admits the two solutions…”

Section: Path Length Computationmentioning

confidence: 99%

Shortest Paths to Obstacles for a Polygonal Dubins Car

Giordano

Vendittelli

2009

IEEE Trans. Robot.

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Abstract-In this paper, we characterize the time-optimal trajectories leading a Dubins car in collision with the obstacles in its workspace. Due to the constant velocity constraint characterizing the Dubins car model, these trajectories form a sufficient set of shortest paths between any robot configuration and the obstacles in the environment. Based on these paths, we define and give the algorithm for computing a distance function that takes into account the nonholonomic constraints and captures the nonsymmetric nature of the system. The developments presented here assume that the obstacles and the robot are polygons although the methodology can be applied to different shapes.

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“…According to Zhang and Mehrjerdi (2013) the coordination and control algorithms can be classified in leader-follower, behavioral based, virtual structure, graph based and potential field based approaches. The leader-follower principle is a well-established approach for non-holonomic mobile robots (Consolini et al 2008;Cui et al 2009), particularly regarding decentralized controllers in order to maintain the flexibility of a distributed system. These decentralized controllers are typically based on feedback linearization (Ge and Cui 2002;Desai et al 1998) or backstepping and can be adapted to different tasks by switching the control law (Das et al 2002).…”

Section: Related Workmentioning

confidence: 99%

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

et al. 2018

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The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver.Keywords Localization and navigation in multi-robot systems · Distributed robotic systems operating on land, sea and air · Multi-robot and multi-vehicle motion coordination · Model predictive control · Sensor constrained control · Unmanned aerial vehicle · Quadrotor

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Leader–follower formation control of nonholonomic mobile robots with input constraints

Cited by 605 publications

References 11 publications

Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system

Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system

Shortest Paths to Obstacles for a Polygonal Dubins Car

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

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