Project Summary Our overarching goal is to develop a robot-assisted workstation with continuum manipulators and novel imaging and visualization techniques to enable access to hard-to-reach regions in the next generation of minimally-invasive orthopaedic surgery procedures. Our goal is to develop systems and devices that enable surgical treatment of bone defects such as femoro-acetabular impingement (FAI), metastatic bone disease, osteonecrosis, and osteolysis. In this application we develop the system for hip arthroscopy surgery, in specific, osteochondroplasty (resecting the femur and acetabulum) to treat FAI. We propose the development of a robot- assisted workstation with arthroscopic navigation that enables adaptive surgical planning and real-time monitoring and execution of the plan for the purposes of treating FAI. FAI is a disease characterized by limited range of motion of the hip due to abnormal anatomy of femur and/or acetabulum that causes obstructing pathological contact. Repetitive microtrauma of osseous convexities may occur during daily living and sport activities. As a consequence of this recurring irritation, the labrum degenerates leading to irreversible chondral damage that progresses and results in degenerative disease of the hip joint if the underlying cause of FAI is not addressed. This disease is the major cause of early osteoarthritis of the hip, especially in young and active patients with an incidence of 54.4 /100000/year. Patients exhibiting FAI are usually in their 20s–40s with an estimated prevalence of 10–15%. Arthroscopic Osteochondroplasty and laberal repair for the treatment of FAI (AO-FAI) is a challenging surgery and involves minimally-invasive resecting of the bone from the acetabulum (pincer deformity) and/or femur (cam deformity) through arthroscopic view to restore impingement-free range of motion. As the number of FAI surgeries and the new surgeons that perform the procedure rapidly increases, it becomes more imperative to develop tools and intraoperative quantification techniques that would facilitate very challenging arthroscopic surgery. Preoperatively, radiographs are commonly used to estimate the amount of resection. Unfortunately, radiographs for diagnosis and precise planning of the amount of bone resection have inherent limitations and inaccuracies. Moreover, because of the limited viewing range and image distortion during arthroscopic surgery coupled with the lack of appropriate technology for precise measurement, accurate execution of the planned amount of bone resection is a challenging task and usually performed by eyeballing. We propose to design a robot-assisted arthroscopic surgery (RAAS) workstation that addresses the above challenges of the FAI surgery.