Robotic Manipulator Proximity Detection Using Circumferential Imagers
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Solution Overview
Problem
Robotic surgical systems with independently movable arms face challenges in monitoring the relative positioning of their components and personnel in the operating room, making it difficult to avoid unintended collisions, especially when force-torque sensors and IMUs are limited to detecting collisions at the distal portions of manipulators.
Innovation Solution
The implementation of a computer vision system using cameras and processors to detect and predict collisions by determining the relative positions of robotic manipulators and personnel, incorporating imagers on the end effectors, and utilizing proximity sensors like LEDs and capacitive sensors to provide alerts and adjust movements to prevent collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If force-torque sensors and IMUs are used to detect collisions, then collision detection capability is improved, but the coverage is limited to distal portions only
Solution Approach 1:
The manipulator is divided into multiple segments with proximity sensors placed at different locations (distal end, proximal end, and intermediate portions). This segmentation allows collision detection coverage throughout the entire manipulator length, overcoming the limitation of single-point sensors.
Solution Approach 2:
Proximity sensors act as intermediary devices between the force-torque sensors and the manipulator body, extending the detection capability to portions not directly instrumented with force-torque sensors. These sensors provide intermediate measurement points that bridge the coverage gap.
2Adaptability or versatility
If multiple proximity sensors are placed throughout the manipulator, then collision detection coverage is improved, but device complexity increases
Solution Approach 1:
The proximity sensors are designed with multi-functionality, serving both as collision detection devices and as position monitoring tools. This universal application reduces the need for separate sensor systems and minimizes overall device complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system monitors changes in proximity parameters (distance to surrounding objects) to detect potential collisions. By focusing on parameter changes rather than absolute positions, the system achieves comprehensive monitoring with fewer sensors, reducing complexity.
3Loss of information
If computer vision systems are used to determine relative positions, then situational awareness is improved, but system complexity and cost increase
Solution Approach 1:
Markers serve as intermediaries between the manipulator and the computer vision system. These simple visual markers are easily detected by cameras and provide sufficient information for relative position determination, avoiding the need for complex vision processing algorithms or expensive sensor systems.
Solution Approach 2:
Instead of using complex active sensors on the manipulator, the system uses passive visual markers that create a simplified optical copy of the manipulator's position. This approach reduces system complexity while maintaining the ability to determine relative positions accurately.
Data Source
AI summary
A proximity detection system allows monitoring of proximity between the end effectors of first and second independent robotic manipulators. Imagers are circumferentially positioned around the end effector of at least one of the robotic manipulators. Image data from the imagers is analyzed to determine proximity between the end effectors. When determined proximity falls below a defined threshold, the system issues an alert to the user or slows/suspends manipulator motion.