Written by Ron Alterovitz and Ken Goldberg, this monograph combines ideas from robotics, physically-based modeling, and operations research to develop new motion planning and optimization algorithms for image-guided medical procedures.

This book constitutes the refereed joint proceedings of the First International Workshop on OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018, 5th International Workshop on Computer Assisted Robotic Endoscopy, CARE 2018, 7th International Workshop on Clinical Image-Based Procedures, CLIP 2018, and the First International Workshop on Skin Image Analysis, ISIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 11 full papers presented at OR 2.0 2018, the 5 full papers presented at CARE 2018, the 8 full papers presented at CLIP 2018, and the 10 full papers presented at ISIC 2018 were carefully reviewed and selected. The OR 2.0 papers cover a wide range of topics such as machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors. The CARE papers cover topics to advance the field of computer-assisted and robotic endoscopy. The CLIP papers cover topics to fill gaps between basic science and clinical applications. The ISIC papers cover topics to facilitate knowledge dissemination in the field of skin image analysis, as well as to host a melanoma detection challenge, raising awareness and interest for these socially valuable tasks.

Surgical robotics is a rapidly evolving field. With roots in academic research, surgical robotic systems are now clinically used across a wide spectrum of surgical procedures. Surgical Robotics: Systems Applications and Visions provides a comprehensive view of the field both from the research and clinical perspectives. This volume takes a look at surgical robotics from four different perspectives, addressing vision, systems, engineering development and clinical applications of these technologies. The book also: -Discusses specific surgical applications of robotics that have already been deployed in operating rooms -Covers specific engineering breakthroughs that have occurred in surgical robotics -Details surgical robotic applications in specific disciplines of surgery including orthopedics, urology, cardiac surgery, neurosurgery, ophthalmology, pediatric surgery and general surgery Surgical Robotics: Systems Applications and Visions is an ideal volume for researchers and engineers working in biomedical engineering.

This book constitutes the refereed proceedings of the 16th Annual Conference on Towards Autonomous Robotics, TAROS 2015, held in Liverpool UK, in September 2015. The 16 revised full papers presented together with 18 short papers were carefully reviewed and selected from 59 submissions. The overall program covers various aspects of robotics, including navigation, planning, sensing and perception, flying and swarm robots, ethics, humanoid robotics, human-robot interaction, and social robotics.

Robotics is undergoing a major transformation in scope and dimension. From a largely dominant industrial focus, robotics is rapidly expanding into human en- ronments and vigorously engaged in its new challenges. Interacting with, assisting, serving, and exploring with humans, the emerging robots will increasingly touch people and their lives. Beyond its impact on physical robots, the body of knowledge robotics has p- duced is revealing a much wider range of applications reaching across diverse research areas and scienti?c disciplines, such as: biomechanics, haptics, neu- sciences, virtual simulation, animation, surgery, and sensor networks among others. In return, the challenges of the new emerging areas are proving an abundant source of stimulation and insights for the ?eld of robotics. It is indeed at the intersection of disciplines that the most striking advances happen. The SpringerTracts in AdvancedRobotics(STAR) is devoted to bringing to the research community the latest advances in the robotics ?eld on the basis of their signi?cance and quality. Through a wide and timely dissemination of critical - search developments in robotics, our objective with this series is to promote more exchanges and collaborations among the researchers in the community and c- tribute to further advancements in this rapidly growing ?eld.

The fully automated estimation of the 6 degrees of freedom camera motion and the imaged 3D scenario using as the only input the pictures taken by the camera has been a long term aim in the computer vision community. The associated line of research has been known as Structure from Motion (SfM). An intense research effort during the latest decades has produced spectacular advances; the topic has reached a consistent state of maturity and most of its aspects are well known nowadays. 3D vision has immediate applications in many and diverse fields like robotics, videogames and augmented reality; and technological transfer is starting to be a reality. This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic: As usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme; namely projective models, spurious rejection, model selection and self-calibration.

This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.