An innovative decentralized strategy for I-AUVs cooperative manipulation tasks

Conti, Roberto; Meli, Enrico; Ridolfi, Alessandro; Allotta, Benedetto

doi:10.1016/j.robot.2015.06.006

Cited by 43 publications

(35 citation statements)

References 32 publications

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“…At each sampling time, the UVMS i ∈ N solves an NMPC scheme subject to its corresponding dynamics (18) and a number of inequality constraints (i.e., (23a)-(23c) and (25)) in order to follow the computed desired trajectory x d O (s) and velocity v d O (s) for a time interval s ∈ [t j , t j + T P ] based on (19), (20) and (21). In particular, in sampled data NMPC, a Finite Horizon Optimal Control Problem (FHOCP) is solved at discrete sampling time instants t j based on the current state measurements x i (t j ), i ∈ N .…”

Section: E Control Designmentioning

confidence: 99%

A Distributed Predictive Control Approach for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems

Heshmati-alamdari

Karras

Kyriakopoulos

2019

2019 International Conference on Robotics and Automation (ICRA)

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This paper addresses the problem of cooperative object transportation for multiple Underwater Vehicle Manipulator Systems (UVMSs) in a constrained workspace involving static obstacles. We propose a Nonlinear Model Predictive Control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations such as: kinematic and representation singularities, obstacles within the workspace, joint limits and control input saturations. More precisely, by exploiting the coupled dynamics between the robots and the object, and using certain load sharing coefficients, we design a distributed NMPC for each UVMS in order to cooperatively transport the object within the workspace's feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Additionally, the feedback relies on each UVMS's locally measurements and no explicit data is exchanged online among the robots, thus reducing the required communication bandwidth. Finally, real-time simulation results conducted in UwSim dynamic simulator running in ROS environment verify the efficiency of the theoretical finding.

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Section: E Control Designmentioning

confidence: 99%

A Distributed Predictive Control Approach for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems

Heshmati-alamdari

Karras

Kyriakopoulos

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Here is reported, for sake of brevity, the list of the modeled sensors: Teledyne Explorer Doppler Velocity Log (DVL): working frequency 10 Hz, noise standard deviation 0.1 cm/s [23]. All the sensor biases have been compensated through offline calibrations.…”

Section: I-auv Modelmentioning

confidence: 99%

A Dynamic Manipulation Strategy for an Intervention: Autonomous Underwater Vehicle

et al. 2015

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This paper presents the modelling and the control architecture of an Autonomous Underwater Vehicle for Intervention (I-AUV). Autonomous underwater manipulation with free-floating base is still an open topic of research, far from reaching an industrial product. Dynamic manipulation tasks, where relevant vehicle velocities are required during manipulation, over an additional challenge. In this paper, the accurate modelling of an I-AUV is described, not neglecting the interaction with the fluid. A grasp planning strategy is proposed and integrated in the control of the whole system. The performances of the I-AUV have been analysed by means of simulations of a dynamic manipulation task.

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“…There is a widespread trend in the use of small low-cost robots rather than a single robot for certain tasks of exploration, localization, formation generation, etc [1,3,5,7,14]. Robot coordination is inspired by the behavior of social animals such as insects and mammals [10].…”

Section: Introductionmentioning

confidence: 99%

Collective Behaviors in Swarms of Builder Robots

Ordaz-Rivas¹,

Rodríguez-Liñán²,

Torres-Treviño³

2019

RCS

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Swarm robotics is inspired by the behavior of social animals for the coordination of a large number of low cost and insufficient robots that in performing a task requires collaboration. The behavior in a swarm of robots can be manipulated by changing the parameters of repulsion, attraction, orientation and influence (RAOI). In the case of repulsion, attraction and orientation modify the basic behavior of the swarm creating functional groups of robots keeping them close or dispersed, even forming chains. While the influence parameter is associated with specific stimuli to guide the swarm to perform simple tasks. To demonstrate this, a simulation platform presents the impact of these parameters in a swarm of builder robots considering a task of transporting materials.

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An innovative decentralized strategy for I-AUVs cooperative manipulation tasks

Cited by 43 publications

References 32 publications

A Distributed Predictive Control Approach for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems

A Distributed Predictive Control Approach for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems

A Dynamic Manipulation Strategy for an Intervention: Autonomous Underwater Vehicle

Collective Behaviors in Swarms of Builder Robots

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