[Paper Review] Raven: Open Surgical Robotic Platforms

Yangming Li, Blake Hannaford+1 more|arXiv (Cornell University)|Jun 27, 2019

Surgical Simulation and Training78 references4 citations

TL;DR

This paper evaluates the impact of the Raven I and II open surgical robotic platforms over the past three years, analyzing trends in robotic surgery research through citation analysis. It identifies mechanical design and teleoperation as dominant research areas, highlights limited adoption of machine learning and autonomy, and discusses challenges in simulation, evaluation, and cybersecurity, offering insights into open research problems and future directions for surgical robotics.

ABSTRACT

The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

Motivation & Objective

• To assess the impact of the Raven I and II open surgical robotic platforms on the research community over the past three years.
• To identify and analyze the most active research topics and emerging challenges in robotic surgery using Raven-related publications as a case study.
• To examine the adoption of advanced technologies such as machine learning, autonomy, simulation, and cybersecurity in robotic surgery research.
• To highlight unmet needs and opportunities in robotic surgery, particularly in relation to open platforms like Raven.
• To guide future research by summarizing trends, contributions, and limitations in the field based on recent literature.

Proposed method

• Conducted a citation-based literature review of 106 peer-reviewed publications citing Raven I and II from the past three years.
• Categorized publications by research topic (e.g., mechanical design, teleoperation, autonomy, simulation, evaluation, cybersecurity) to identify trends.
• Analyzed the technical focus of cited works, including control architecture, sensor integration, and system safety mechanisms.
• Evaluated the role of open-source software and hardware in enabling innovation, particularly through ROS compatibility and real-time Linux control.
• Assessed the use of the Raven platform for simulation, training, and remote access, including ongoing development of the Raven simulator and online interface.
• Identified gaps in research, such as low adoption of deep learning and soft robotics, and discussed barriers like data scarcity and reliability concerns.

Experimental results

Research questions

• RQ1 What are the most prominent research topics in robotic surgery that have used the Raven platforms in the past three years?
• RQ2 How has the open-source nature of Raven I and II influenced innovation and collaboration in surgical robotics research?
• RQ3 Why is machine learning, particularly deep learning, underutilized in current robotic surgery research despite its success in broader robotics?
• RQ4 What are the key challenges and unmet needs in advancing autonomy, simulation, and cybersecurity in surgical robotic systems?
• RQ5 How do the design and control features of Raven I and II shape the direction and limitations of current robotic surgery research?

Key findings

• Mechanical design and modeling are the most active research areas, with 38% of cited works focusing on improving dexterity, stability, and control precision of surgical robots.
• Teleoperation remains the dominant control paradigm, with 28% of cited works addressing system architecture, master controllers, and communication protocols.
• Cybersecurity is emerging as a critical concern, with 6% of cited works specifically analyzing threats and defenses in telesurgical systems using the Raven platform.
• Autonomous robotic surgery is underexplored, with only 10% of cited works focusing on motion planning or task automation, such as needle insertion and suturing.
• Simulation and evaluation are significantly underrepresented, with only 3% of cited works dedicated to simulation and 2% to system evaluation, indicating a gap in validation and training tools.
• The open-source nature of Raven’s software and hardware has enabled widespread adoption across 18 research institutions, fostering innovation in control, safety, and sensor integration.