Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Saska, Martin; Krajník, Tomáš; Vonásek, Vojtĕch; Kasl, Zdenĕk; Spurný, Vojtěch; Přeučil, Libor

doi:10.1007/s10846-013-9976-6

Cited by 27 publications

(16 citation statements)

References 36 publications

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“…Remark 6: We notice that if the desired trajectories satisfy the temporal separation requirement, i.e., (3), then the result given in Theorem 1 ensures intervehicle collision avoidance. In fact, upon knowledge of: i) the quality-of-service of the communication network [i.e., and in (17)] and ii) the performance of the given path-following controller [see (14)], one can choose in (3) large enough so as to guarantee that the vehicles will never collide throughout the mission.…”

Section: Resultsmentioning

confidence: 99%

“…For this reason, in recent years, the topic has been the subject of considerable research and development effort, especially in terms of control and communication technologies. Relevant work includes spacecraft formation flying [1]- [3], UAV control [4], [5], coordinated control of land robots [6]- [8], and control of multiple autonomous underwater vehicles [9], [10]. Research on cooperative flight of multirotor teams is particularly extensive (see [2], [3], [11]- [16], and references therein).…”

Section: Introductionmentioning

confidence: 99%

“…1, where the vehicles are required to follow two paths of different lengths, while coordinating along the axis. In this paper, we aim at providing a solution which-differently from other works in the literature [2], [3], [8], [15], [16]-tackles the problem of decentralized cooperative control with time-varying communication networks through a Lyapunov-based approach, thus providing rigorous performance bounds as a function of the quality-of-service of the communication network. Moreover, we address the problem of non-ideal tracking performance of the UAVs, by showing that the time-coordination guarantees are retained even when the UAV does not converge-but remains close-to the desired position.…”

Section: Introductionmentioning

confidence: 99%

“…(c) Coordination errors computed as and .Fig. 10(c) shows the convergence of and to the desired rate 1, as well as the convergence of the coordination errors to a neighborhood of zero 3. …”

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confidence: 98%

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Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Cichella

Kaminer

Dobrokhodov

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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This paper addresses the problem of time-coordination of a team of cooperating multirotor unmanned aerial vehicles that exchange information over a supporting time-varying network. A distributed control law is developed to ensure that the vehicles meet the desired temporal assignments of the mission, while flying along predefined collision-free paths, even in the presence of faulty communication networks, temporary link losses, and switching topologies. In this paper, the coordination task is solved by reaching consensus on a suitably defined coordination state. Conditions are derived under which the coordination errors converge to a neighborhood of zero. Simulation and flight test results are presented to validate the theoretical findings.Note to Practitioners-This paper presents an approach which enables a fleet of multirotor UAVs to follow a set of desired trajectories and coordinate along them, thus satisfying specific spatial and temporal assignments. The proposed solution can be employed in applications in which multiple vehicles are tasked to execute cooperative, collision-free maneuvers, and accomplish a common goal in a safely manner. An example is sequential monitoring, in which the UAVs have to visit and monitor a set of points of interest, while maintaining a desired temporal separation between each other. In this paper, we also simulate a scenario in which the vehicles, positioned in a square room, are required to exchange position with each other. It is shown that the proposed control algorithm not only ensures that the UAVs arrive at the final destinations at the same time, but also guarantees safety, i.e., the vehicles avoid collision with each other at all times.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(c) Coordination errors computed as and .Fig. 10(c) shows the convergence of and to the desired rate 1, as well as the convergence of the coordination errors to a neighborhood of zero 3. …”

mentioning

confidence: 98%

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Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Cichella

Kaminer

Dobrokhodov

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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“…The proposed formation control mechanism is suited for the real-world deployment of autonomous robots relying on the onboard visual relative localization, which brings additional movement constraints to the MAV team. The method is based on a leader-follower technique, where the team of robots is stabilized by sharing knowledge of the leader's position within the formation (see the original leader-follower approach [41] designed for a group of ground robots (UGVs) and the extension of the leader-follower approach for heterogenous MAVs-UGVs teams in [42], [43] for details). The method presented in this section is an extension of our work introduced in conference paper [44], where only simulation results were presented and where the requirements on the onboard relative localization necessary for the HW experiments, which is the main contribution of this paper, were not included.…”

Section: Multi-robot Scenarios Demonstrating the Practical Usabilmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

Self Cite

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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Coordinated and Cooperative Control of Heterogeneous Mobile Manipulators

Molina

Ortiz

2018

Social Robotics

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This paper proposes a multilayer scheme for the cooperative control of 2 n  heterogeneous mobile manipulators that allows to transport an object in common in a coordinated way; for which the kinematic modeling of each mobile manipulator robot is performed. Stability and robustness are demonstrated using the Lyapunov theory in order to obtain asymptotically stable control. Finally, the results are presented to evaluate the performance of the proposed control, which confirms the scope of the controller to solve different movement problems.

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Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Cited by 27 publications

References 36 publications

Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

Coordinated and Cooperative Control of Heterogeneous Mobile Manipulators

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