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Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots
Jianfeng Zhou1, Quan Nguyen2, Sanjana Kamath2
1Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, United States.
Frontiers in Robotics and AI
|May 2, 2022
Summary
This study introduces a finger-controlled interface for hexapod robots, enhancing leg placement control. The Tip Position Mapping (TPM) method proved efficient for obstacle avoidance, reducing leg contacts in complex environments.
Area of Science:
- Robotics
- Human-Robot Interaction
- Control Systems
Background:
- Current human-robot interfaces for legged robot control demand significant mental effort and physical exertion.
- Specifying precise leg placement is crucial for effective robot locomotion and stability.
Purpose of the Study:
- To develop and evaluate a novel human-robot interface for controlling hexapod robot leg placement using finger motions.
- To compare the efficiency and performance of two proposed mapping methods: Joint Angle Mapping (JAM) and Tip Position Mapping (TPM).
Main Methods:
- Two distinct mapping methods, JAM and TPM, were developed to translate finger movements into hexapod leg placements.
- The TPM method was evaluated for its efficiency with lab staff.
- A Webots simulation compared TPM-based manual gait with fixed and camera-based autonomous gaits for obstacle avoidance, recording Number of Contacts (NOC).
Main Results:
- The Tip Position Mapping (TPM) method demonstrated superior efficiency compared to JAM.
- Both camera-based autonomous gait and TPM effectively adjusted step size for obstacle avoidance.
- In environments with high obstacle density, TPM significantly reduced leg contacts (to 25% of fixed gaits), outperforming some autonomous gaits.
Conclusions:
- The TPM interface offers a more intuitive and less strenuous method for controlling legged robot leg placement.
- TPM shows significant potential for improving obstacle avoidance in challenging terrains, especially when autonomous systems falter.
- Future enhancements could involve integrating haptic feedback, additional degrees of freedom, and artificial intelligence for further performance gains.
