Trajectory Planner for Type II Singularities Avoidance Based on Output Twist Screws

Pulloquinga, José L.; Escarabajal, Rafael J.; Vallés, Marina; Valera, Ángel; Mata, Vicente

doi:10.1007/978-3-031-08140-8_48

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…2) Define the desired behavior of the admittance model, which can differ for each cartesian coordinate, encoded in ⃗ t s adm and ⃗ G adm (with t s adm λ >> t seva ). Obtain the transfer functions H λ using equation (29).…”

Section: Combined System For Controlmentioning

confidence: 99%

“…The four transfer functions H λ are obtained using equation (29). The four TWSs $T for this PR are calculated as:…”

Section: B Model Specifications and Test Trajectoriesmentioning

confidence: 99%

“…The whole purpose and the contribution of this paper is the tracking of position and force reference profiles while avoiding Type II singularities with a PR, both tasks exploiting the DMP advantages and dynamic behavior. There exist methods for offline planning trajectories free of singularities [29], which cannot adapt to online changes like those happening with force control. Also, some strategies have been proposed to cross singular configurations [30].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combined Admittance Control With Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives

Escarabajal¹,

Pulloquinga²,

Valera³

et al. 2023

IEEE Trans. Robot.

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This paper addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human-robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: i) the force exerted on the human must be controlled and limited, and ii) Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the Dynamic Movement Primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises.

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Section: Combined System For Controlmentioning

confidence: 99%

“…The four transfer functions H λ are obtained using equation (29). The four TWSs $T for this PR are calculated as:…”

Section: B Model Specifications and Test Trajectoriesmentioning

confidence: 99%