Point and Go: Intuitive Reference Frame Reallocation in Mode Switching for Assistive Robotics

TL;DR

Point and Go mode switching redefines WMRA control by introducing an end-effector–anchored translation frame and a new rotation frame, enabling intuitive, human-like movements without mode switching. The rotation frame uses a realigned $[x_2,y_2,z_2]$ basis controlled by a simple $ heta_{align}$ alignment and a PID, while translations combine base-height and horizontal-plane motion with a sweeping $R_{y_3}$ wrist motion to guide trajectories. Position-based rotation control provides precise, undoable orientation adjustments within a home pose, capped by a maximum angular excursion $ ext{±} ext{ } ext{ }\\alpha$. Across ablation tests and a three-task user study, PnG significantly reduces completion times, pauses, and mode switches compared with Cartesian mode switching and performs on par with or better than a state-of-the-art learning method in basic tasks, indicating strong practical potential for home and clinical use.

Abstract

Operating high degree of freedom robots can be difficult for users of wheelchair mounted robotic manipulators. Mode switching in Cartesian space has several drawbacks such as unintuitive control reference frames, separate translation and orientation control, and limited movement capabilities that hinder performance. We propose Point and Go mode switching, which reallocates the Cartesian mode switching reference frames into a more intuitive action space comprised of new translation and rotation modes. We use a novel sweeping motion to point the gripper, which defines the new translation axis along the robot base frame's horizontal plane. This creates an intuitive `point and go' translation mode that allows the user to easily perform complex, human-like movements without switching control modes. The system's rotation mode combines position control with a refined end-effector oriented frame that provides precise and consistent robot actions in various end-effector poses. We verified its effectiveness through initial experiments, followed by a three-task user study that compared our method to Cartesian mode switching and a state of the art learning method. Results show that Point and Go mode switching reduced completion times by 31\%, pauses by 41\%, and mode switches by 33\%, while receiving significantly favorable responses in user surveys.

Figures (5)

• Figure 1: Point and Go control axes consisting of wrist rotations about ${y_3}$ that 'points' the end-effector to 'go' along the axis defined by $z_3$.
• Figure 2: Rotations along the $x_1$-axis for different end-effector orientations in Cartesian mode switching. Rotating the end-effector pose by 90 degrees between a) and b) causes different robot motions for the same joystick input in each case.
• Figure 3: Rotations along the $x_2$-axis for different end-effector orientations in the new coordinate frame. Robot motions stay consistent regardless of end-effector pose, leading to more intuitive control
• Figure 4: Experimental setup and tasks for the ablation experiments and user study.
• Figure 5: Radar plots showing median Likert survey responses (1-least favorable, 7-most favorable)