Approximating Robot Reachable Space Using Convex Polytopes

Skurić, Antun; Padois, Vincent; Daney, David

doi:10.1007/978-3-031-22731-8_4

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…pycapacity has been used in several scientific papers, for real-time control of collaborative carrying using two Franka Emika Panda robots (Skuric et al, 2021), for developing an assist-asneeded control strategy for collaborative carrying task of the human operator and the Franka robot (Skuric et al, 2022). The package has also been used to calculate the approximation of the robot's reachable space using convex polytope (Skuric et al, 2023). On the other hand, the package has been used for the biomechanical calibration of the human musculoskeletal models (Laisné et al, 2023).…”

Section: Statement Of Needmentioning

confidence: 99%

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Pycapacity: a real-time task-space capacity calculation package for robotics and biomechanics

Skuric,

Padois,

Daney

2023

JOSS

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This paper presents a Python package called pycapacity, which provides a set of tools for evaluating task-space physical ability metrics for humans and robots, based on polytopes and ellipsoids. The aim of pycapacity is to provide a set of efficient tools for their evaluation in an easy to use framework that can be easily integrated with standard robotics and biomechanics libraries. The package implements several state of the art algorithms for polytope evaluation that bring many of the polytope metrics to the few milliseconds evaluation time, making it possible to use them in online and interactive applications.

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Section: Statement Of Needmentioning

confidence: 99%

“…• Robot's reachable space approximation in the desired horizon of interest Δ𝑡 ℎ using the convex polytope formulation 𝑃 𝑥 , described in the paper by (Skuric et al, 2023)…”

Section: Polytopesmentioning

confidence: 99%

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Pycapacity: a real-time task-space capacity calculation package for robotics and biomechanics

Skuric,

Padois,

Daney

2023

JOSS

Self Cite

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Prediction of pose errors implied by external forces applied on robots: towards a metric for the control of collaborative robots

Fortineau,

Padois,

Daney

2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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The presented work tackles the question of quantifying the pose deviations of robots subject to external disturbance forces. While this question may not be central for large robots perfectly rejecting disturbances through high controller gains, it is an important factor when considering collaborative settings where smaller robots may be deviated from their task because of unmodeled physical interactions. This is all the more true with human-robot collaboration where human capacities may fluctuate over time and have to be compensated by a proper adaptation of the robot control. To move forward in this direction, this work first derives a deviation prediction methodology and exemplifies it using three largely employed control approaches. The proposed prediction method is then validated using simulated and real robot experiments both in single and multiple robots cases. The obtained results constitute a stepping stone towards a quantitative metric for robots adapting their behaviour to human motor fluctuation.

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Approximating Robot Reachable Space Using Convex Polytopes

Cited by 2 publications

References 9 publications

Pycapacity: a real-time task-space capacity calculation package for robotics and biomechanics

Pycapacity: a real-time task-space capacity calculation package for robotics and biomechanics

Prediction of pose errors implied by external forces applied on robots: towards a metric for the control of collaborative robots

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