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An Interactive Hands-Free Controller for a Riding Ballbot to Enable Simple Shared Control Tasks

Chenzhang Xiao, Seung Yun Song, Yu Chen, Mahshid Mansouri, Joao Ramos, William R. Norris, Elizabeth T. Hsiao-Wecksler

TL;DR

iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

Abstract

Our team developed a riding ballbot (called PURE) that is dynamically stable, omnidirectional, and driven by lean-to-steer control. A hands-free admittance control scheme (HACS) was previously integrated to allow riders with different torso functions to control the robot's movements via torso leaning and twisting. Such an interface requires motor coordination skills and could result in collisions with obstacles due to low proficiency. Hence, a shared controller (SC) that limits the speed of PURE could be helpful to ensure the safety of riders. However, the self-balancing dynamics of PURE could result in a weak control authority of its motion, in which the torso motion of the rider could easily result in poor tracking of the command speed dictated by the shared controller. Thus, we proposed an interactive hands-free admittance control scheme (iHACS), which added two modules to HACS to improve the speed-tracking performance of PURE: control gain personalization module and interaction compensation module. Human riding tests of simple tasks, idle-keeping and speed-limiting, were conducted to compare the performance of HACS and iHACS. Two manual wheelchair users and two able-bodied individuals participated in this study. They were instructed to use "adversarial" torso motions that would tax the SC's ability to keep the ballbot idling or below a set speed. In the idle-keeping tasks, iHACS demonstrated minimal translational motion and low command speed tracking RMSE, even with significant torso lean angles. During the speed-limiting task with command speed saturated at 0.5 m/s, the system achieved an average maximum speed of 1.1 m/s with iHACS, compared with that of over 1.9 m/s with HACS. These results suggest that iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

An Interactive Hands-Free Controller for a Riding Ballbot to Enable Simple Shared Control Tasks

TL;DR

iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

Abstract

Our team developed a riding ballbot (called PURE) that is dynamically stable, omnidirectional, and driven by lean-to-steer control. A hands-free admittance control scheme (HACS) was previously integrated to allow riders with different torso functions to control the robot's movements via torso leaning and twisting. Such an interface requires motor coordination skills and could result in collisions with obstacles due to low proficiency. Hence, a shared controller (SC) that limits the speed of PURE could be helpful to ensure the safety of riders. However, the self-balancing dynamics of PURE could result in a weak control authority of its motion, in which the torso motion of the rider could easily result in poor tracking of the command speed dictated by the shared controller. Thus, we proposed an interactive hands-free admittance control scheme (iHACS), which added two modules to HACS to improve the speed-tracking performance of PURE: control gain personalization module and interaction compensation module. Human riding tests of simple tasks, idle-keeping and speed-limiting, were conducted to compare the performance of HACS and iHACS. Two manual wheelchair users and two able-bodied individuals participated in this study. They were instructed to use "adversarial" torso motions that would tax the SC's ability to keep the ballbot idling or below a set speed. In the idle-keeping tasks, iHACS demonstrated minimal translational motion and low command speed tracking RMSE, even with significant torso lean angles. During the speed-limiting task with command speed saturated at 0.5 m/s, the system achieved an average maximum speed of 1.1 m/s with iHACS, compared with that of over 1.9 m/s with HACS. These results suggest that iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.
Paper Structure (16 sections, 8 equations, 7 figures, 2 tables)

This paper contains 16 sections, 8 equations, 7 figures, 2 tables.

Figures (7)

  • Figure 1: Rider on PURE during acceleration-deceleration test translating forward. The movement is controlled via torso learning, i.e., forward to drive, backward to brake. PURE is composed of a ballbot drivetrain that uses a spherical wheel for maneuvering, and a torso-dynamics estimation system (TES) to achieve a hands-free lean control with tunable sensitivity.
  • Figure 2: Simple illustrations of possible shared controller (SC) to enhance rider safety. (a) Idling scene: SC sets the command speed to zero when the system aims to remain in a fixed location, resisting any translational motion induced by rider torso movement. (b) Speed-limiting scene: SC adjusts the magnitude of the translational movement to avoid collision.
  • Figure 3: Modeling of rider-ballbot system in the sagittal plane. (a) Complete model of rider and PURE. (b) Isolated model of PURE: interaction between robot and rider represented by forces and moments applied at point P.
  • Figure 4: Control block diagram for (a) hand-free admittance control scheme (HACS) and (b) the proposed interactive hands-free admittance control scheme (iHACS). The command speed generator has tunable parameters and calculates the command translational speed for the low-level LQR-PI baseline balance controller for the ballbot drivetrain. The iHACS includes two additional modules: (1) the control gain personalization module that derives new LQR control gains based on the measurement of rider weight at the initial calibration stage, and (2) the interaction compensation module that generates the full state command vector $\mathbf{s_{cy}^*} = [\theta_{EQy}, 0, 0, \dot\phi_{cy}]$ and compensation torque $\mathbf{\tau_{EQy}}$ for the low-level controller.
  • Figure 5: (a) Test course illustration of speed-limiting tasks. Green and red projections represented “go” and “stop” maneuvers, respectively. (b) Participant riding PURE with a mobile harness system.
  • ...and 2 more figures