Smoother Position-Drift Compensation for Time Domain Passivity Approach based Teleoperation
February 06, 2020 Β· Declared Dead Β· π IEEE/RJS International Conference on Intelligent RObots and Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andre Coelho, Harsimran Singh, Tin Muskardin, Ribin Balachandran, Konstantin Kondak
arXiv ID
2002.02296
Category
cs.RO: Robotics
Cross-listed
eess.SY
Citations
32
Venue
IEEE/RJS International Conference on Intelligent RObots and Systems
Last Checked
4 months ago
Abstract
Despite being one of the most robust methods in bilateral teleoperation, Time Domain Passivity Approach (TDPA) presents the drawback of accumulating position drift between master and slave devices. The lack of position synchronization poses an obstacle to the performance of teleoperation and may prevent the successful accomplishment of such tasks. Several techniques have been developed in order to solve the position-drift problem in TDPA-based teleoperation. However, they either present poor transparency by over-conservatively constraining force feedback or add high impulse-like force signals that can be harmful to the hardware and to the human operator. We propose a new approach to compensate position drift in TDPA-based teleoperation in a smoother way, which keeps the forces within the normal range of the teleoperation task while preserving the level of transparency and the robust stability of energy-based TDPA. We also add a way of tuning the compensator to behave in accordance with the task being performed, whether it requires faster or smoother compensation. The feasibility and performance of the method were experimentally validated. Good position tracking and regular-amplitude forces are demonstrated with up to 500 ms round-trip constant and variable delays for hard-wall contacts.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Robotics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
π
π
The Cartographer
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
π
π
The Cartographer
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
π
π
The Cartographer
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
R.I.P.
π»
Ghosted
Learning agile and dynamic motor skills for legged robots
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted