Optimal exciting motion for fast robot identification. Application to contact painting tasks with estimated external forces

Katsumata, Takuma; Navarro, Benjamin; Bonnet, Vincent; Fraisse, Philippe; Crosnier, André; Venture, Gentiane

doi:10.1016/j.robot.2018.11.021

Cited by 18 publications

(4 citation statements)

References 20 publications

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“…Since we use a KUKA LWR4+ API 3 that provides measurements only of joint angles, the joint velocity and The experiments were run with a sampling time of 5 ms. The parameters of the adaptive controller, given by ( 24) and ( 26 T , and the robot dynamic model was obtained from [48]. For the outer loop, the matrices in (18) were chosen empirically as M d = 1.5I 6 , B d = 300I 6 , and K d = 100I 6 .…”

Section: Resultsmentioning

confidence: 99%

Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

Fonseca

Adorno

Fraisse

2021

J Intell Robot Syst

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Whenrobots physically interact with the environment, compliant behaviors should be imposed to prevent damages to all entities involved in the interaction. Moreover, during physical interactions, appropriate pose controllers are usually based on the robot dynamics, in which the ill-conditioning of the joint-space inertia matrix may lead to poor performance or even instability. When the control is not precise, large interaction forces may appear due to disturbed end-effector poses, resulting in unsafe interactions. To overcome these problems, we propose a task-space admittance controller in which the inertia matrix conditioning is adapted online. To this end, the control architecture consists of an admittance controller in the outer loop, which changes the reference trajectory to the robot end-effector to achieve a desired compliant behavior; and an adaptive inertia matrix conditioning controller in the inner loop to track this trajectory and improve the closed-loop performance. We evaluated the proposed architecture on a KUKA LWR4+ robot and compared it, via rigorous statistical analyses, to an architecture in which the proposed inner motion controller was replaced by two widely used ones. The admittance controller with adaptive inertia conditioning presents better performance than with a controller based on the inverse dynamics with feedback linearization, and similar results when compared to the PID controller with gravity compensation in the inner loop.

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Section: Resultsmentioning

confidence: 99%

Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

Fonseca

Adorno

Fraisse

2021

J Intell Robot Syst

Self Cite

View full text Add to dashboard Cite

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“…Furthermore, the robot has simulated its motion on MATLAB within SolidWorks design and it was using a quadratic function with a velocity profile called a trajectory plan of the robot as in [5]. In 2019 [6], the dynamic model obtained the appropriate inertia parameters of the Kuka LWR robot and its final efficacy. The geometric parameters also distinguished as shown in the tables the fastest time by defining those parameters as the joint torque was estimated with the RMS difference.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Spraying Robot

Jasim

Ali

Hamad

2022

alkej

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An indoor spraying robot is built in this research to solve numerous challenges associated with manual spraying. The mechanical, hardware and essential technologies used are all detailed and designed. The proposed spraying robot's conceptual design is split into two parts: hardware and software. The mechanical design, manufacturing, electrical, and electronics systems are described in the hardware part, while the control of the robot is described in the software section. This robot's kinematic and dynamic models were developed using three links that move in the x, y, and z directions. The robot was then designed using SolidWorks software to compute each connection's deflection and maximum stresses. The characteristics of the stepper motors, power screw and belt drive, are calculated. Finally, an Arduino-Nano controller and stepper motor actuators were used to build and run the robot. As a result, the robot was able to move smoothly vertically and horizontally, according to the findings of the experiments as shown in figures 22, 23, 24, and 25. These figures showed the position and velocity curves of the links of the robot.

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“…The theoretical absolute minimum value of the cost function is 1: this information is useful because it allows us to determine how close a real experiment is to theoretical optimality. In practice, a value of C < 100 is typically considered good, whereas C < 10 is optimal [47,54].…”

Section: Optimal Free-motion Excitationmentioning

confidence: 99%

“…This result is very good w.r.t. identification best practices [47,54], thus different, and possibly more efficient, optimization techniques were not explored.…”

Section: Optimal Excitation Computationmentioning

confidence: 99%