Academic journal article Human Factors

Speed-Accuracy Characteristics of Human-Machine Cooperative Manipulation Using Virtual Fixtures with Variable Admittance

Academic journal article Human Factors

Speed-Accuracy Characteristics of Human-Machine Cooperative Manipulation Using Virtual Fixtures with Variable Admittance

Article excerpt


A goal of human-machine cooperative systems is to enhance user performance of tasks at the limits of human motor control. For example, microsurgical procedures such as retinal vein cannulation require operation at scales that exceed the capabilities of all but the most skilled surgeons (Weiss, 2001). Even macro-scale tasks, such as tracking a tool along a curve, can be mentally and physically demanding. Tremor and fatigue greatly affect accuracy and completion time during tracking tasks (Riviere, Rader, & Thakor, 1998). Robots can be more precise and untiring than humans, but the complexity of tasks they can perform in unstructured environments is restricted, given the limitations of artificial intelligence. Human-machine cooperative systems are designed to integrate the intelligence and experience of humans, as well as the precision and accuracy of robots, by allowing shared control between the human operator and the robot. Cooperative systems are especially useful in small-scale tasks such as microassembly and microsurgery, in which visual and haptic feedback to the user is limited. Our research extends the assistance provided by cooperative systems through the application of virtual fixtures.

Virtual fixtures are a means of providing assistance to a human operator in a cooperative or teleoperated manipulation system. Using an admittance-controlled robot, we have developed "guidance" virtual fixtures that assist human operators in the execution of tasks related to microsurgery (Bettini, Lang, Okamura, & Hager, 2001). In our admittance controller, the velocity output is proportional to the input force through an admittance gain. A human user operates the robot by pushing on a handle, as shown in Figure 1. Computer vision is used to detect a planar path to be followed, and an admittance control law with a configuration-dependent anisotropic gain matrix provides the user with physical assistance to stay on that path. The virtual fixtures can provide differing levels of guidance; complete guidance strictly prevents the user from deviating from the reference path, whereas no guidance allows the user to move with equal ease in any direction. The system was created as a human-centered design (Sheridan, 2000), even though it is possible for the robot to run autonomously The details of this system, as configured for our experiments, are provided in the Method section.


In this study, two experiments were performed to investigate the relationship between virtual fixture admittance and performance. Experiment 1 was designed to determine this relationship for a path-following task. Experiment 2 compared the effect of different levels of virtual fixture guidance on user performance in three general tasks: path following, off-path targeting, and avoidance. We hypothesize that the complete guidance virtual fixture will result in the lowest error and execution time for the path-following task and that a high level of guidance will increase error and execution time for the off-path targeting and the avoidance tasks. Based on the experimental results, we created models that can be used to select the appropriate level of guidance to maximize speed and accuracy, based on the nature of the task.

Related Work

Virtual fixtures have been previously applied to telemanipulation systems. Rosenberg (1993) provided an implementation of virtual fixtures for a peg-in-hole task in a teleoperated environment. The virtual fixtures were implemented using auditory feedback and impedance planes (virtual walls with stiffness and damping properties) on a haptic device master. Experimental results showed that the virtual fixtures improve task performance by as much as 70% as compared with when no fixtures are present.

Park, Howe, and Torchiana (2001) applied a virtual wall to the slave robot of a teleoperated surgical system, defining the position of the virtual fixture as the location of the internal mammary artery obtained from a preoperative CT scan. …

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