Handheld robots improve precision and safety in micromanipulation tasks
Active Constraints for Simultaneous Guidance and Safety in Handheld Micromanipulation
Robotics
Summary
Micromanipulation tasks like microsurgery require very steady and accurate hand movements, which can be challenging for a person alone. The authors developed a handheld robot that helps guide the user's hand while keeping it safe from mistakes at the same time. They used a mathematical optimization method to control the robot so it can both guide movement and prevent unsafe motions within a limited workspace. Tests with a medical-like task showed the safety was greatly improved and accuracy was recovered compared to previous methods. This approach could make delicate tasks safer and easier to do with handheld robots.
What this means in practice
- •For medical device engineers: Develop handheld surgical robots that improve precision and safety simultaneously during delicate procedures.
- •For precision manufacturing teams: Integrate active constraints in handheld tools to enhance operator accuracy and prevent damage in fine assembly tasks.
Authors
Roger Ho, Robert A. MacLachlan, Cameron N. Riviere
Abstract
In micromanipulation, erroneous hand motion limits unaided manipulation accuracy, which can affect task performance and safety in critical applications like microsurgery. Handheld robots (micromanipulators) enable motion accuracy beyond human limits by providing robotic assistance in a low-cost, compact, intuitive form factor. Particularly, active constraints are an effective form of robotic assistance, which aids the operator by restricting the robot's permissible motion based on task-specific requirements or objectives, thereby providing task guidance and enforcing safety. However, existing formulations of active constraints for handheld systems are limited, most notably because of the inability to provide guidance and enforce safety simultaneously. Implementing active constraints with optimization-based methods can address these limitations, but existing implementations are designed for free-standing robots, and neglect that handheld robots actuate relative to the operator's hand with a limited workspace. In this work, we propose an optimization-based active constraint framework for handheld micromanipulation that enables simultaneous guidance and safety enforcement. The formulation was implemented in a handheld robot and demonstrated on a medically relevant artificial task. Results show that the proposed formulation reduced safety violation rate by more than two orders of magnitude relative to baselines, while recovering the threefold accuracy loss from enforcing safety alone.