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Displacement information is essential in many robotic applications involving contact, especially with viscoelastic objects. In this paper, we provide a novel displacement estimation method using contact pressure distribution. Our method can estimate displacement using only a typical tactile sensor, which can be shared by a force measurement. As a result, our method can reduce the number of required sensors for stiffness estimation, so it is suitable for stiffness-required applications with strict spatial constraints. Simulation and experimental results demonstrate the performance of our method. [ABSTRACT FROM AUTHOR]

The demand for high-performance impedance control in electro-hydraulic actuation systems is a mainstream issue in the robot actuation research community. Although various nonlinear model-based impedance controllers have been developed and showed good performance in recent years, they remain hindered by the high cost of force sensors, and accurate internal dynamics models need to be derived to overcome the non-backdrivability and complex dynamics of hydraulic systems. As a solution to these problems, this paper presents an impedance control approach for hydraulic systems that require no external force sensors or chamber pressure sensors with a pressure control servovalve or backdrivable servovalve. First, we show that a backdrivable servovalve brings inherent backdrivability to hydraulic systems, such that high-performance impedance control can be implemented without requirement of force sensors and exact knowledge on the dynamics of hydraulic chambers, owing to its torque-source property and low effective valve inertia on the link side. Second, utilizing the obtained backdrivability, we develop a passivity-guaranteed impedance controller. A momentum-based external torque observation algorithm is incorporated to render desired impedance over a wide range, without using force/torque sensor. The performance of the proposed backdrivable servovalve and impedance controller was verified through extensive experimental results. [ABSTRACT FROM AUTHOR]

This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot, which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via force/torque (F/T) sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1-DoF testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload. [ABSTRACT FROM AUTHOR]

Impedance control allows robots to manipulate physical interactions delicately. However, issues associated with path planning and impedance remain unresolved. Herein we propose a path and impedance planning method for impedance control in a robot based on programming by demonstration through telemanipulation using a surface electromyogram. We considered a task that requires quick and precise adjustment of path and impedance, that is, ball trapping. We implemented a teleoperated robot that can deliver an operator's impedance as well as position during the ball trapping task to the slave side. The operators were asked to perform demonstrations of ball-trapping tasks using the implemented teleoperated robot, where the slave side is a vertical robot with one degree of freedom. The path and impedance were recorded and programmed as control input profiles from a set of successful demonstrations using Gaussian mixture regression. The result showed that using the human demonstration, the robot could learn how to catch a dropped ball without rebounding. [ABSTRACT FROM AUTHOR]