Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Force, Torque and Contacts in Grasping and Assembly
Object Motion-Decoupled Internal Force Control for a Compliant Multifingered HandCompliance in multifingered hand improves grasp stability and effectiveness of the manipulation tasks. Compliance of robotic hands depends mainly on the joint control parameters, on the mechanical design of the hand, as joint passive springs, and on the contact properties. In object grasping the primary task of the robotic hand is the control of internal forces which allows to satisfy the contact constraints and consequently to guarantee a stable grasp of the object. When compliance is an essential element of the multifingered hand, and the control of the internal forces is not designed to be decoupled from the object motion, it happens that a change in the internal forces causes the object trajectory to deviate from the planned path with consequent performance degradation. This paper studies the structural conditions to design an internal force controller decoupled from object motions. The analysis is constructive and a controller of internal forces is proposed. We will refer to this controller as object motion-decoupled control of internal forces. The force controller has been successfully tested on a realistic model of the DLR Hand II. This controller provides a trajectory interface allowing to vary the internal forces (and to specify object motions) of an underactuated hand, which can be used by higher-level modules, e.g. planning tools.
Robust, Inexpensive Resonant Frequency Based Contact Detection for Robotic ManipulatorsThis paper presents a method for detecting contact on a compliant link utilizing a method to sense changes in the resonant frequency of the link due to external contact. The approach uses an inexpensive accelerometer mounted on or inside the compliant link and a phase locked loop circuit to oscillate the link at its resonant frequency. Using this approach, we are able to reliably sense contact anywhere on the link with a contact force threshold sensitivity of between 0.05 and 0.15 N depending on the contact location.
Testing Pressurized Spacesuit Glove Torque with an Anthropomorphic Robotic HandWhile robotic hands have been developed for manipulation and grasping, their potential as tools for performance evaluation of engineered products - particularly compliant garments that are not easily modeled â€“ has not been broadly studied. In this research, the development of a low-cost anthropomorphic robotic hand is introduced that is designed to characterize glove stiffness in a pressurized environment. The anthropomorphic robotic hand was designed to mimic a human hand in a neutral posture corresponding to the naturally relaxed position in zero gravity, and includes the transverse arch, longitudinal arch, and oblique flexion of the rays. The resulting model also allows for realistic donning and doffing of the prototype spacesuit glove, its pressurization, and torque testing of individual joints. Solid models and 3D printing enabled the rapid design iterations necessary to successfully work with the compliant pressure garment. The performance of the robotic hand is experimentally demonstrated with a spacesuit glove for different levels of pressures, and a unique data processing method is used to calculate the required actuator torque at each finger's knuckle joint. The reliable measurement method confirmed that glove finger torque increases as the internal pressure increases. The proposed robotic design and method provide an objective and systematic way of evaluating the performance of compliant gloves.
Learning Grasping Force from DemonstrationThis paper presents a novel force learning framework to learn fingertip force for a grasping and manipulation process from a human teacher with a force imaging approach. A demonstration station is designed to measure fingertip force without attaching force sensor on fingertips or objects so that this approach can be used with daily living objects. A Gaussian Mixture Model (GMM) based machine learning approach is applied on the fingertip force and position to obtain the motion and force model. Then a force and motion trajectory is generated with Gaussian Mixture Regression (GMR) from the learning result. The force and motion trajectory is applied to a robotic arm and hand to carry out a grasping and manipulation task. An experiment was designed and carried out to verify the learning framework by teaching a Fanuc robotic arm and a BarrettHand a pick-and-place task with demonstration. Experimental results show that the robot applied proper motions and forces in the pick-and-place task from the learned model.
Revised Force Control Using a Compliant Sensor with a Position Controlled RobotA different way of force control is presented, that is especially advantageous for position controlled robots. Instead of usual force control laws we rely on the well tuned position control loop and just use the force sensor to measure the target pose or to predict the desired trajectory. In combination with a compliant sensor we introduce an inherently stable framework of force control which almost inhibits all control errors. After an unexpected impact the force error is reduced independently from the sensor's bandwidth or delays in signal processing. Thus the (inevitable) impact force is more significant than the measured force control errors. The special case of a sensor that is mounted far away from a vertex-face contact is discussed, too.
Force Controlled Robotic Assembly without a Force SensorThe traditional way of controlling an industrial robot is to program it to follow desired trajectories. This approach is sufficient as long as the accuracy of the robot and the calibration of the workcell is good enough. In robotic assembly these conditions are usually not fulfilled, because of uncertainties, e.g., variability in involved parts and objects not gripped accurately. Using force control is one way to handle these difficulties. This paper presents a method of doing force control without a force sensor. The method is based on detuning of the low-level joint control loops, and the force is estimated from the control error. It is experimentally verified in a small part assembly task with a kinematically redundant robotic manipulator.