Distributed formation building algorithms for groups of wheeled mobile robots

Savkin, Andrey V.; Wang, Chao; Baranzadeh, Ahmad; Xi, Zhiyu; Nguyen, Hung T.

doi:10.1016/j.robot.2015.08.006

Cited by 37 publications

(39 citation statements)

References 32 publications

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“…In this work, flocking provided the asymptotic stability for formation control and formation shape was achieved by a synchronizing velocity vector of individual vehicles. In another publication, considering the problem of decentralized flocking and global formation building of the group of wheeled robots, a randomized decentralized navigation algorithm was presented where autonomous vehicles move in the same direction with the same speed, thus forming a formation [77]. Here, each robot did not know a priori its position in the desired configuration, and the robots attained consensus on their positions via local information exchange.…”

Section: Flockingmentioning

confidence: 99%

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Soni

2018

Robotics

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Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

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Section: Flockingmentioning

confidence: 99%

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Soni

2018

Robotics

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“…1 Recent applications are seen in the control of pattern formation of fleets of autonomous car-like vehicles or swarms of unmanned aerial vehicles for tasks such as aiding traffic management of automated highways, environmental monitoring, search-and-rescue in hazardous environments, and area coverage and reconnaissance. [2][3][4] An exciting development in this area of research is the use of swarm principles to design the controllers. Animal swarming is considered an instance of collective selforganization, which is defined in the study by Camazine et al 5 as "a broad range of pattern-formation processes in both physical and biological systems, with the pattern being an emergent property of the system, rather than a property imposed on the system by an external ordering influence."…”

Section: Introductionmentioning

confidence: 99%

Stable emergent formations for a swarm of autonomous car-like vehicles

Vanualailai

2019

International Journal of Advanced Robotic Systems

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A biological swarm is an ideal multi-agent system that collectively self-organizes into bounded, if not stable, formations. A mathematical model, developed appropriately from some principle of swarming, should enable one, therefore, to study formation strategies for multiple autonomous robots. In this article, based on the hypothesis that swarming is an interplay between long-range attraction and short-range repulsion between the individuals in the swarm, a planar individual-based or Lagrangian swarm model is constructed using the Direct Method of Lyapunov. While attraction ensures the swarm is cohesive, meaning that the individuals in the swarm remain close to each other at all times, repulsion ensures that the swarm is well-spaced, meaning that no two individuals in the swarm occupy the same space at the same time. Via a novel Lyapunov-like function with attractive and repulsive components, the article establishes the global existence, uniqueness, and boundedness of solutions about the centroid. This paves the way to prove that the swarm model, governed by a system of first-order ordinary differential equations (ODEs), is cohesive and well-spaced. The article goes on to show that the artificial swarm can collectively self-organize into two stable formations: (i) a constant arrangement about the centroid when the system has equilibrium points, and (ii) a highly parallel formation when the system does not have equilibrium points. Computer simulations not only illustrate these but also reveal other emergent patterns such as swirling structures and random-like walks. As an application, we tailor the model accordingly and propose new autonomous steering laws giving rise to pattern-forming for multiple nonholonomic car-like vehicles.

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“…On the contrary, the decentralized control considers that the formation characteristics are shared among the participants at a very high level, where the control actions to maintain the desired grouping are calculated for each vehicle individually. Although both configurations require some type of intercommunication to ensure satisfactory execution, the second case requires high reliability of data exchange to achieve the control objectives [17]. A considerable advantage of decentralized control is the integration of different control methods to the group of robots, taking into account that generally for cooperative robotics, the most important aspect is to maintain the position of the robotic set into a trajectory or path.…”

Section: Introductionmentioning

confidence: 99%

Multirobot Heterogeneous Control Considering Secondary Objectives

Acosta

Rivera

Andaluz

et al. 2019

Sensors

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Cooperative robotics has considered tasks that are executed frequently, maintaining the shape and orientation of robotic systems when they fulfill a common objective, without taking advantage of the redundancy that the robotic group could present. This paper presents a proposal for controlling a group of terrestrial robots with heterogeneous characteristics, considering primary and secondary tasks thus that the group complies with the following of a path while modifying its shape and orientation at any time. The development of the proposal is achieved through the use of controllers based on linear algebra, propounding a low computational cost and high scalability algorithm. Likewise, the stability of the controller is analyzed to know the required features that have to be met by the control constants, that is, the correct values. Finally, experimental results are shown with different configurations and heterogeneous robots, where the graphics corroborate the expected operation of the proposal.

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Distributed formation building algorithms for groups of wheeled mobile robots

Cited by 37 publications

References 32 publications

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Stable emergent formations for a swarm of autonomous car-like vehicles

Multirobot Heterogeneous Control Considering Secondary Objectives

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