The inherent uncertainty associated with unstructured environments makes establishing a successful grasp difficult. Traditional approaches to this problem involve hands that are complex, fragile, require elaborate sensor suites, and are difficult to control. Alternatively, by carefully designing the mechanical structure of the hand to incorporate features such as compliance and adaptability, the uncertainty inherent in unstructured grasping tasks can be more easily accommodated. In this paper, we demonstrate a novel adaptive and compliant grasper that can grasp objects spanning a wide range of size, shape, mass, and position/orientation using only a single actuator. The hand is constructed using polymer-based Shape Deposition Manufacturing (SDM) and has superior robustness properties, making it able to withstand large impacts without damage. We also present the results of two experiments to demonstrate that the SDM Hand can reliably grasp objects in the presence of large positioning errors, while keeping acquisition contact forces low. In the first, we evaluate the amount of allowable manipulator positioning error that results in a successful grasp. In the second experiment, the hand autonomously grasps a wide range of spherical objects positioned randomly across the workspace, guided by only a single image from an overhead camera, using feed-forward control of the hand.
▪ Abstract Robotic technology is enhancing surgery through improved precision, stability, and dexterity. In image-guided procedures, robots use magnetic resonance and computed tomography image data to guide instruments to the treatment site. This requires new algorithms and user interfaces for planning procedures; it also requires sensors for registering the patient’s anatomy with the preoperative image data. Minimally invasive procedures use remotely controlled robots that allow the surgeon to work inside the patient’s body without making large incisions. Specialized mechanical designs and sensing technologies are needed to maximize dexterity under these access constraints. Robots have applications in many surgical specialties. In neurosurgery, image-guided robots can biopsy brain lesions with minimal damage to adjacent tissue. In orthopedic surgery, robots are routinely used to shape the femur to precisely fit prosthetic hip joint replacements. Robotic systems are also under development for closed-chest heart bypass, for microsurgical procedures in ophthalmology, and for surgical training and simulation. Although results from initial clinical experience is positive, issues of clinician acceptance, high capital costs, performance validation, and safety remain to be addressed.
Robotic catheters have the potential to revolutionize cardiac surgery by enabling minimally invasive structural repairs within the beating heart. This paper presents an actuated catheter system that compensates for the fast motion of cardiac tissue using 3-D ultrasound image guidance. We describe the design and operation of the mechanical drive system and catheter module and analyze the catheter performance limitations of friction and backlash in detail. To mitigate these limitations, we propose and evaluate mechanical and control-system compensation methods, which include inverse and model-based backlash compensation, to improve the system performance. Finally, in vivo results are presented, which demonstrate that the catheter can track the cardiac tissue motion with less than 1-mm rms error. The ultimate goal of this research is to create a fast and dexterous robotic catheter system that can perform surgery on the delicate structures inside of the beating heart.