Path-Constrained Haptic Motion Guidance via Adaptive Phase-Based Admittance Control

Robots have surpassed humans in terms of strength and precision, yet humans retain an unparalleled ability for decision-making in the face of unpredictable disturbances. This article aims to combine the strengths of both entities within a singular task: human motion guidance under strict geometric constraints, particularly adhering to predetermined paths. To tackle this challenge, a modular haptic guidance law is proposed that takes the human-applied wrench as an input. Using an auxiliary variable called phase, the generated desired motion is guaranteed to consistently adhere to the constraint path. It is demonstrated how the guidance policy can be generalized into physically interpretable terms, adjustable either prior to initiating the task or dynamically while the task is in progress. Additionally, an illustrative guidance adaptation policy is showcased that takes into account the human's manipulability. Leveraging passivity analysis, potential sources of instability are pinpointed, and subsequently, overall system stability is ensured by incorporating an augmented virtual energy tank. Lastly, a comprehensive set of experiments, including a 20-participant user study, explores various aspects of the approach in practice, encompassing both technical and usability considerations.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Robotics
Published
2024-12-24
DOI
https://doi.org/10.1109/tro.2024.3521861
Citations
7
Primary Topic
Teleoperation and Haptic Systems
Type
article
Field-Weighted Citation Impact
1.49

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Path-Constrained Haptic Motion Guidance via Adaptive Phase-Based Admittance Control

Sami Haddadin, Seyed Ali Baradaran Birjandi, Erfan Shahriari, Matej Hoffmann et al.
7 citations
IEEE Transactions on Robotics
Teleoperation and Haptic Systems
1.49
article

Path-Constrained Haptic Motion Guidance via Adaptive Phase-Based Admittance Control

Sami Haddadin, Seyed Ali Baradaran Birjandi, Erfan Shahriari, Matej Hoffmann, Petr Švarný
article en
7 citations

Abstract

Robots have surpassed humans in terms of strength and precision, yet humans retain an unparalleled ability for decision-making in the face of unpredictable disturbances. This article aims to combine the strengths of both entities within a singular task: human motion guidance under strict geometric constraints, particularly adhering to predetermined paths. To tackle this challenge, a modular haptic guidance law is proposed that takes the human-applied wrench as an input. Using an auxiliary variable called phase, the generated desired motion is guaranteed to consistently adhere to the constraint path. It is demonstrated how the guidance policy can be generalized into physically interpretable terms, adjustable either prior to initiating the task or dynamically while the task is in progress. Additionally, an illustrative guidance adaptation policy is showcased that takes into account the human's manipulability. Leveraging passivity analysis, potential sources of instability are pinpointed, and subsequently, overall system stability is ensured by incorporating an augmented virtual energy tank. Lastly, a comprehensive set of experiments, including a 20-participant user study, explores various aspects of the approach in practice, encompassing both technical and usability considerations.

IEEE Transactions on RoboticsVol. 41
Czech Technical University in Prague (CZ), Technical University of Munich (DE)
Grantová Agentura České Republiky
Climate action
Openalex Percentile: Top 19%
Teleoperation and Haptic Systems
1.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.