Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network.

Area of Science:

• Robotics
• Control Systems
• Sensor Networks

Background:

• Hyper-redundant robots require advanced shape sensing for precise control.
• Existing methods can be computationally intensive and complex to implement.
• Real-time feedback is crucial for dynamic robotic applications.

Purpose of the Study:

• To develop a novel, computationally efficient shape sensing approach for hyper-redundant robots.
• To enable direct kinematic parameter calculation in operational space.
• To facilitate real-time feedback systems for enhanced robot control.

Main Methods:

• Embedding an Attitude and Heading Reference System (AHRS) Inertial Measurement Unit (IMU) sensor network into the robot structure.
• Directly calculating robot kinematic parameters in operational space using sensor data.
• Validating the kinematic model and shape using Hardware-in-the-Loop (HIL) techniques and external sensory systems.

Main Results:

• Demonstrated feasibility of using an embedded IMU sensor network for robot shape sensing.
• Achieved reduced computational time for kinematic parameter calculation.
• Validated the accuracy of the proposed shape sensing approach through experimental testing.

Conclusions:

• The proposed AHRS IMU sensor network approach is effective for hyper-redundant robot shape sensing.
• The method offers a computationally efficient solution for real-time applications.
• This technique enhances the potential for advanced control and pose estimation in hyper-redundant robots.