A Nonlinear Model Predictive Control scheme for cooperative manipulation with singularity and collision avoidance

2018 European Control Conference (ECC)

2018

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This paper addresses the problem of cooperative transportation of an object rigidly grasped by N robotic agents. We propose a Nonlinear Model Predictive Control (NMPC) scheme that guarantees the navigation of the object to a desired pose in a bounded workspace with obstacles, while complying with certain input saturations of the agents. The control scheme is based on inter-agent communication and is decentralized in the sense that each agent calculates its own control signal. Moreover, the proposed methodology ensures that the agents do not collide with each other or with the workspace obstacles as well as that they do not pass through singular configurations. The feasibility and convergence analysis of the NMPC are explicitly provided. Finally, simulation results illustrate the validity and efficiency of the proposed method.

Section: Resultssupporting

confidence: 60%

Communication-based Decentralized Cooperative Object Transportation Using Nonlinear Model Predictive Control

Verginis

2018 European Control Conference (ECC)

2018

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“…2). The desired MITL formula is set as: ϕ = [0,∞) {¬obs} ∧ ♦ [6,12] {goal 1 } ∧ ♦ [20,30] Fig. 2 depicts the workspace with RoI, unsafe regions, the nominal trajectory of the robot (orange color), the real trajectory of the robot (black color) and the tube centered along the nominal trajectory.…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust Tube-based Model Predictive Control for Time-constrained Robot Navigation

2019 American Control Conference (ACC)

2019

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This paper deals with the problem of time-constrained navigation of a robot modeled by uncertain nonlinear non-affine dynamics in a bounded workspace of R n . Initially, we provide a novel class of robust feedback controllers that drive the robot between Regions of Interest (RoI) of the workspace. The control laws consists of two parts: an on-line controller which is the outcome of a Finite Horizon Optimal Control Problem (FHOCP); and a backstepping feedback law which is tuned off-line and guarantees that the real trajectory always remains in a bounded hyper-tube centered along the nominal trajectory of the robot. The proposed controller falls within the so-called tube-based Nonlinear Model Predictive control (NMPC) methodology. Then, given a desired high-level specification for the robot in Metric Interval Temporal Logic (MITL), by utilizing the aforementioned controllers, a framework that provably guarantees the satisfaction of the formula is provided. The proposed framework can handle the rich expressiveness of MITL in both safety and reachability specifications. Finally, the proposed framework is validated by numerical simulations.

“…Virtual Control Inputs ν 1 (t) 2 ν 2 (t) 2 q(t) 2 Fig. 5: The virtual control input signals ν 1 (t) 2 , ν 2 (t) 2 and q(t) 2 of the kinematic model (3). It holds that ν 1 2 ≤ 2, ν 2 2 ≤ 2 and q 2 ≤ 2.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

A Tube-based MPC Scheme for Interaction Control of Underwater Vehicle Manipulator Systems

Verginis

2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)

2018

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Over the last years, the development of Autonomous Underwater Vehicles (AUV) with attached robotic manipulators, the so-called Underwater Vehicle Manipulator System (UVMS), has gained significant research attention, due to the ability of interaction with underwater environments. In such applications, force/torque controllers which guarantee that the end-effector of the UVMS applies desired forces/torques towards the environment, should be designed in a way that state and input constraints are taken into consideration. Furthermore, due to their complicated structure, unmodeled dynamics as well as external disturbances may arise. Motivated by this, we proposed a robust Model Predicted Control Methodology (NMPC) methodology which can handle the aforementioned constraints in an efficient way and it guarantees that the end-effector is exerting the desired forces/torques towards the environment. Simulation results verify the validity of the proposed framework.