Geometrical Postural Optimisation of 7-DoF Limb-Like Manipulators

TL;DR

This paper introduces a geometrical postural optimization method for 7-DoF limb-like manipulators that enhances robustness to singularities and computational efficiency, inspired by biological motor control insights.

Contribution

It presents a novel geometrical framework with decoupled strategies for optimizing limb posture, improving manipulability and robustness in complex robotic tasks.

Findings

The method is robust across thousands of configurations.

It effectively manages singularities and orientation control.

Numerical tests validate accuracy and efficiency.

Abstract

Robots are moving towards applications in less structured environments, but their model-based controllers are challenged by the tasks' complexity and intrinsic environmental unpredictability. Studying biological motor control can provide insights into overcoming these limitations due to the high dexterity and stability observable in humans and animals. This work presents a geometrical solution to the postural optimisation of 7-DoF limbs-like mechanisms, which are robust to singularities and computationally efficient. The theoretical formulation identified two separate decoupled optimisation strategies. The shoulder and elbow strategy align the plane of motion with the expected plane of motion and guarantee the reachability of the end-posture. The wrist strategy ensures the end-effector orientation, which is essential to retain manipulability when nearing a singular configuration. The numerical results confirmed the theoretical observations and allowed us to identify the effect of different grasp strategies on system manipulability. The geometrical method was numerically tested in thousands of configurations proving to be both robust and accurate. The tested scenarios include left and right arm postures, singular configurations, and walking scenarios. The proposed geometrical approach can find application in developing efficient and robust interaction controllers that could be applied in computational neuroscience and robotics.