Localization of steerable catheters in minimally invasive surgery is critical with respect to patient safety, surgeon manipulation, and procedural efficacy. While there are many potential benefits to patients including shorter recovery times, less tissue trauma, and lower infection rates than traditional surgeries, localization of surgical tools is still an area of much research. Current technology offers several sensory modalities. However, each system has drawbacks which do not provide a clear best practice. This research focuses on incorporating redundant commonplace surgical sensing technologies to reduce the likelihood of errors, failures, or inherent sensor characteristics causing harm to the patient and/or surgeon while providing accurate localization. Dual particle filters are implemented using both a fluoroscopic-like stereo imaging system and an electromagnetic pose sensor for measurement updates in a prototype catheter testbed. A previously developed catheter model is modified to increase accuracy in the particle filter outputs which are combined using a weighted average based on each filter's particle statistics. Experimental results implementing the combined particle filter multi-modality algorithm in feedback control validates the algorithms ability to provide accurate localization in a surgical setting while overcoming sensor limitations and possible failure modes.

Robotic catheters are gaining widespread use in the medical community for cardiac, neurological and other surgical interventions. However, many of the catheters used in these procedures exhibit non-linear behavior, and thus present many difficulties in implementing effective open-loop control. Systems such as this have been shown to benefit from closed-loop control, however very little investigation has been done into 3D closed loop control of this class of manipulators. Initial investigations by the authors have shown greatly improved positioning accuracy and response with closed-loop control based on feedback from an electromagnetic localization sensor. This paper describes the control approach and experimental results, and provides a robotic catheter system overview.

In order to successfully perform visual servoing of robot manipulators the control algorithm must account for the physical limitations of the manipulator. These constraints define the robot workspace boundary. Any visual servoing control algorithm must avoid this boundary if stability of motion is to be ensured. In the paper, a method is developed by which a desired object path can be interpolated between two arbitrary object poses based on image feature extraction. This path is defined to be a continuous change in object pose between the initial and final desired poses which avoids the workspace boundary. The development of this method involves the parameterization of the 2-D displacement transformation or homography. By decomposing this homography a set of object path poses can be interpolated in either the image plane or reconstructed in the 3-D workspace. Implementation of a visual servoing procedure confirms the validity of the interpolated path with respect to workspace boundary avoidance.

Visual-servoing tasks for mobile robots characteristically require the processing of vast amounts of navigational information which can impede the performance of even the most well designed systems. This paper investigates the idea of using a simple human-based strategy to guide a mobile robot to intercept a projectile. This vision based control strategy relies on a single image-based parameter whose sign corresponds to a decision whether to run forward or backward for successful interception. Assuming the ball can be tracked easily in real time by the robot's vision system, motion decision making and control computations are minimized. We examine and develop several motion control strategies which incorporate the robot's velocity and acceleration in addition to this human-based strategy. The simplest strategy is tested on a mobile robot and demonstrates results similar to a human.