An Under-Actuated Whippletree Mechanism Gripper based on Multi-Objective Design Optimization with Auto-Tuned Weights
This addresses the need for more adaptable rigid grippers in robotics applications, though it appears to be an incremental improvement focused on a specific climbing task.
The researchers tackled the problem of rigid grippers lacking flexibility by developing an under-actuated whippletree mechanism gripper optimized for a one-wall climbing task, achieving sufficient grasping forces through a multi-objective design optimization with auto-tuned weights.
Current rigid linkage grippers are limited in flexibility, and gripper design optimality relies on expertise, experiments, or arbitrary parameters. Our proposed rigid gripper can accommodate irregular and off-center objects through a whippletree mechanism, improving adaptability. We present a whippletree-based rigid under-actuated gripper and its parametric design multi-objective optimization for a one-wall climbing task. Our proposed objective function considers kinematics and grasping forces simultaneously with a mathematical metric based on a model of an object environment. Our multi-objective problem is formulated as a single kinematic objective function with auto-tuning force-based weight. Our results indicate that our proposed objective function determines optimal parameters and kinematic ranges for our under-actuated gripper in the task environment with sufficient grasping forces.