Force feedback devices for use in virtual reality have been the design, which enable the user to interact with the virtual environment in real-time. We have developed a virtual environment for lumbar puncture, a widely used medical technique involving the insertion of a 3 inch long needle in the spinal region of a patient involving real-time 3D display of the spinal region and real-time force feedback. The trainee can insert a specialized joystick needle in a virtual spine and simultaneously "feel" a realistic force response, matching the real procedure. Our goal is to be able to control the motor such that the whole system "feels" like a needle penetrating the human tissue. In order to create a realistic simulation involving force feedback we show how force feedback theory can be applied to our virtual environment.

Force control in robotic mechanisms has been extensively researched. There have been several results which have concentrated either on explicit force control or impedance control. Further, many of these works require accurate identification of the stiffness of the environment. In this work the authors examine the role of adaptive controllers for force control with unknown system and environment parameters and clearly integrate several fundamental issues such as explicit force control, impedance control, and impedance control combined with a desired force control. All of these issues are treated using standard model-reference adaptive control (MRAC) approach. Within the same framework, the authors analyze other equally fundamental issues such as environment stiffness identification, stability of the complete system, and parameter convergence. The authors demonstrate the effectiveness of the proposed theory through experiments.

Lumbar puncture for purposes of administering spinal or epidural anesthesia is a complex clinical skill which requires the clinician to precisely correlate a detailed mental map of hidden three-dimensional anatomy with tactile feedback from the spinal needle as it's being inserted. At Dartmouth, a collaborative work between the engineering and medical schools is aimed at developing a computer-based simulation utilizing high-resolution 3D graphics and a force-feedback device to simulate the "feel" of inserting the needle. This paper describes the mechanical design, hardware and software issues and some of the technical challenges being addressed in this project.>

An environment developed to plan and execute decision-making strategies in real time for the control and coordination of a system comprising several mechanical subsystems is described. The tracking of planned trajectories is decoupled from the planning phase through the use of decentralized controllers which are explicitly designed for trackings. They communicate with a central processor within which resides the framework through which the control engineer can program the task and constraints. The planner uses three types of inputs to determine the control signals: the ultimate goal and the desired mode of reaching the goal which the designer must input (such as minimum time), the constraint set which is also input by the designer and transformed by the planner to linear inequality constraints in the acceleration subspace, and methods of solving the constrained problem to generate desired trajectories. Some examples are presented. The environment permits easy incorporation of other constraints which are planned for the future, such as complaint motion and force control.>

The use of neural networks as an alternative to the computation-intensive control-theoretic approach to motion planning and execution is discussed. A three-tiered network structure is proposed: one tier is for conversion of task-space coordinates to joint-space coordinates, one is for converting the start and goal positions in joint space to a dynamic sequence, and one is for enabling the manipulator to execute the sequence by generating the necessary control signals. The issues involved in implementation are discussed and some results are presented. It is concluded that the neural-network approach has shown some promise in its ability to tackle nonlinear static problems and as a tool for generating plans. However, its effectiveness in eliminating completely any kind of online computation for precise control seems doubtful.>