Neizrazito adaptivno upravljanje silom dodira slijednih mehanizama s jednim stupnjem slobode gibanja

The paper presents position/force control with a completely fuzzified adaptive force control system for the single degree of freedom servo mechanisms. The proposed force control scheme contains an adaptive fuzzy force controller and a subordinated fuzzy velocity controller. By using a second-order reference model, a model reference-based fuzzy adaptation mechanism is able to keep the error between the model and system output responses within desired limits. The results obtained by computer simulations indicate a stable performance of the force control system for a wide range of environment stiffness variations. The proposed adaptive force control method has also been effective in case of a contact with a rough surface or a complex form workpiece.Članak prikazuje upravljanje položajem/silom dodira slijednog mehanizma s jednim stupnjem slobode gibanja korištenjem neizrazitog adaptivnog sustava upravljanja silom. Predložena shema upravljanja silom dodira sadrži adaptivni neizraziti regulator sile i podređeni neizraziti regulator brzine vrtnje. Koristeći referentni model drugog reda, neizraziti na modelu zasnovani adaptacijski mehanizam u stanju je držati razliku između odziva modela i odziva sustava u zadanim granicama. Rezultati dobiveni numeričkim simulacijama pokazuju stabilno vladanje sustava upravljanja silom dodira za široki raspon varijacija krutosti okoline. Predložena metoda adaptivnog upravljanja silom se pokazala uspješnom i u slučaju dodira s neravnom površinom ili s radnim predmetom složena oblika

Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

On the formulation of parallel position/force control schemes for industrial manipulators

In this paper, three commonly-used position/force control schemes namely Impedance, Admittance and Hybrid Position/Force control are investigated for use in industrial manipulators. In order to eliminate the instability problem that may occur in the customary versions of these schemes for large position errors, a modification is proposed, which is based on determining the joint-space position errors using inverse kinematic solutions rather than using the inverse Jacobian matrix. The feasibility of this modification relies on the fact that almost all of the industrial manipulators have easily obtainable inverse kinematic solutions. The simulation results showing the performance of the modified control schemes are also presented as applied on a Puma 560 manipulator

GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory

\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1

The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993

Human Motor Control and the Design and Control of Backdriveable Actuators for Human-Robot Interaction

The design of the control and hardware systems for a robot intended for interaction with a human user can profit from a critical analysis of the human neuromotor system and human biomechanics. The primary observation to be made about the human control and ``hardware’’ systems is that they work well together, perhaps because they were designed for each other. Despite the limited force production and elasticity of muscle, and despite slow information transmission, the sensorimotor system is adept at an impressive range of motor behaviors. In this thesis I present three explorations on the manners in which the human and hardware systems work together, hoping to inform the design of robots suitable for human-robot interaction. First, I used the serial reaction time (SRT) task with cuing from lights and motorized keys to assess the relative contribution of visual and haptic stimuli to the formation of motor and perceptual memories. Motorized keys were used to deliver brief pulse-like displacements to the resting fingers, with the expectation that the proximity and similarity of these cues to the response motor actions (finger-activated key-presses) would strengthen the motor memory trace in particular. Error rate results demonstrate that haptic cues promote motor learning over perceptual learning. The second exploration involves the design of an actuator specialized for human-robot interaction. Like muscle, it features series elasticity and thus displays good backdrivability. The elasticity arises from the use of a compressible fluid while hinged rigid plates are used to convert fluid power into mechanical power. I also propose impedance control with dynamics compensation to further reduce the driving-point impedance. The controller is robust to all kinds of uncertainties. The third exploration involves human control in interaction with the environment. I propose a framework that accommodates delays and does not require an explicit model of the musculoskeletal system and environment. Instead, loads from the biomechanics and coupled environment are estimated using the relationship between the motor command and its responses. Delays inherent in sensory feedback are accommodated by taking the form of the Smith predictor. Agreements between simulation results and empirical movements suggests that the framework is viable.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120675/1/gloryn_1.pd