Human-Robot Collaboration based on Robust Motion Intention Estimation with Prescribed Performance

Mavridis, Christos N.; Alevizos, Konstantinos; Bechlioulis, Charalampos P.; Kyriakopoulos, Kostas J.

doi:10.23919/ecc.2018.8550610

Cited by 9 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. ] T that satisfy e j (0) < ρ j (0) and incorporate the desired transient and steady-state performance specifications, the estimation laws (29) and (30) guarantee that e j (t) < ρ j (t) , ∀t ≥ 0.…”

Section: Problem Statement and Control Methodologymentioning

confidence: 99%

“…Apparently, our setup does not allow quantifiable results for 6DoF trajectory tracking as the ones presented in ref. [29] 4 for 1DoF due to the lack of means to create a 6DoF reference trajectory (i.e., augmented reality). In that sense, the results of this experimental study are qualitative.…”

Section: Cooperative Transportation Verificationmentioning

confidence: 99%

“…Regarding ref [29],. although the proposed estimator remains the same, the impedance control formulation is different for consistency with the extended experimental verification that was carried out in this work.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation

Alevizos¹,

Bechlioulis²,

Kyriakopoulos³

2020

Robotica

Self Cite

View full text Add to dashboard Cite

SUMMARY Cooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, the motion intention estimation is utilized in the object dynamics to determine the interaction force between the human and the object. Human effort reduction is then achieved via an impedance control scheme that employs the aforementioned estimations. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements at its end effector, without incorporating any other information on the task. Moreover, an adaptive control scheme is adopted to relax the need for exact knowledge of the object dynamics. Finally, an extension for multiple robotic coworkers is studied and verified via simulation, while extensive experimental results for the single robot case clarify the proposed method and corroborate its efficiency.

show abstract

Section: Problem Statement and Control Methodologymentioning

confidence: 99%

Section: Cooperative Transportation Verificationmentioning

confidence: 99%

See 1 more Smart Citation

Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation

Alevizos¹,

Bechlioulis²,

Kyriakopoulos³

2020

Robotica

Self Cite

View full text Add to dashboard Cite

show abstract

“…16 In a study by Mavridis et al, the desired trajectory of a manipulator was estimated via a prescribed performance estimation law, and the problem of physical human-robot cooperation for object manipulation was solved by an impedance control scheme based on the aforementioned estimation. 17 Relying solely on implicit communication, a decentralized leader-follower architecture in which the desired trajectory of the follower was estimated via a prescribed performance estimation law was presented by Tsiamis et al to address the problem of cooperative object manipulation. 18 Nevertheless, the tracking errors in the four aforementioned studies [15][16][17][18] only converged to a residual set via the corresponding prescribed performance estimation law, which may restrict applications of prescribed performance methodology when high precision is required.…”

Section: Introductionmentioning

confidence: 99%

“…17 Relying solely on implicit communication, a decentralized leader-follower architecture in which the desired trajectory of the follower was estimated via a prescribed performance estimation law was presented by Tsiamis et al to address the problem of cooperative object manipulation. 18 Nevertheless, the tracking errors in the four aforementioned studies [15][16][17][18] only converged to a residual set via the corresponding prescribed performance estimation law, which may restrict applications of prescribed performance methodology when high precision is required.…”

Section: Introductionmentioning

confidence: 99%

Adaptive task‐space cooperative tracking control for manipulators with a desired trajectory estimator and a velocity observer

Zhao

Xiu

Wang

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, the task‐space cooperative tracking control problem of robotic manipulators without velocity measurements is investigated, where the desired trajectory of an unknown object is only available to the root manipulator. A cooperative tracking control scheme is presented, which is composed of a task‐space desired trajectory estimator, a task‐space velocity observer and an adaptive cooperative control law. The estimator is presented to provide the desired trajectory for each manipulator, and the observer is designed to obtain the task‐space velocity in the absence of velocity measurements. Based on the estimator and velocity observer, an adaptive cooperative control law is proposed to satisfy task tracking and motion synchronization of manipulators in the presence of kinematic and dynamic uncertainties. Furthermore, strict stability analysis is conducted to prove that the task‐space tracking and synchronization errors converge to zero under a connected directed graph. Finally, simulation results are given to demonstrate the superiority of the proposed scheme.

show abstract