Computer Vision and Robotics in Perioperative Process

The following sections are included:

• Preface
• Acknowledgments
• Contents
• List of Figures
• List of Tables

The following sections are included:

• Background
• Current Perioperative Process
• Our Goal
  • Increased Safety
  • Improved Throughput
  • Enhanced Care Quality
• Our Approach
  • Move Dirty Instrument to Sorting Table - Step 1
  • Sort the Instruments - Step 2
  • Move Sorted Instruments to Cleaner - Step 3
  • Wash and Disinfect Instruments - Step 4 & 5
  • Move Cleaned Instruments to Kit Building Table - Step 6
  • Build Surgical Kits - Step 7
  • Sterilization - Step 8
• Organization of the Rest of the Book

This chapter describes a human-supervisory distributed robotic software architecture, which has been applied in our multi-agent robotic system to automate the daily and repeated sterilization process [Tan et al. (2015a)]. Each robot is considered as an independent agent to perform assigned tasks with its own capability and coordinate their operations with other robots to ensure that the main process of the workflow satisfy the overall operation requirements. This layered architecture highlights human factors in the automation workflow to provide a flexible and robust human-knowledge-based supervision and control for safe, reliable, and automated process for healthcare industry.

In this chapter, we detail a robust integrated system named TrayBot to autonomously perform manipulation of assets; specifically, transporting reusable surgical instrument trays in the sterile processing center of a hospital. Our method is based on a cognitive decision making mechanism that plans and coordinates the motions of the robot base and the robot manipulator at specific processing locations. A vision-based manipulator control algorithm is developed for the robot to reliably locate and subsequently pick up surgical tool trays. Furthermore, to compensate for perception and navigation errors, we developed a robust self-aligning end effector that allows for improved error-tolerance in larger workspaces.

In this chapter, We describe a novel single-view computer vision algorithm that identifies the next instrument to grip from a cluttered pile. This research was motivated by the challenges of perioperative process in hospitals today. Current process of instrument counting, sorting, and sterilization is highly labor intensive. Improperly sterilized instruments have resulted in many cases of infections. To address these challenges, an integrated robotic system for sorting instruments in a cluttered tray is designed and implemented. A digital camera is used to capture an image of a cluttered tray. A novel single-view vision algorithm is used to detect the instruments and determines the top instrument. Position and orientation of the top instrument is transferred to a robot. Experiments have demonstrated high success rate of both instrument recognition.

In this chapter, we introduced two robotic systems that are capable of picking-up surgical instruments from a tray and placing the surgical instruments into different locations according to the instrument types. One of the robots is used on the dirty side of the sterile processing center and it is implemented using a RethinkRobotics^® Baxter research robot. The other robot is used on the clean side of the processing and it is implemented using a clean room certified Adept^® Viper arm. The pick-n-place manipulation is integrated with the vision component detailed in Chapter 4 and a special electromagnetic gripper. We tested our system in a lab-based environment and the system performance satisfies the requirements of the project.

In this chapter, we discuss a vision-based cognitive system for robots to work in human-existing environment and keep the safety of the sterile processing nurses. An integrated system is implemented with perception, recognition, reasoning, decision-making, and action.

After developing all the technologies discussed in the previous chapters, we moved the robots to Orlando and demonstrated the prototype system in the VA hospital there. This chapter details the demonstration results and some of the challenges encountered by the team during the demonstration.

The following sections are included:

• Bibliography
• Index