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Wheelchair Maneuvering with a Single-Spherical-Wheeled Balancing Mobile Manipulator

Cunxi Dai, Xiaohan Liu, Roberto Shu, Ralph Hollis

TL;DR

This work tackles safe and efficient wheelchair assistance using a single-spherical-wheeled, dynamically balancing CMU ballbot. It offers a holistic method that combines quasi-static whole-body planning, a task-space impedance arm controller, a balancing CoM controller, online EKF-based wheel chair parameter identification, and a pushing pose optimizer plus steering controller to coordinate pushing with body lean. Key contributions include accurate velocity tracking under varying loads, online estimation of wheelchair mass and friction, and demonstrable safety and smoothness of the passenger experience in both open and cluttered environments. The approach enables compact, agile assistance in tight spaces, potentially reducing caregiver burden and expanding mobility for wheelchair users.

Abstract

In this work, we present a control framework to effectively maneuver wheelchairs with a dynamically stable mobile manipulator. Wheelchairs are a type of nonholonomic cart system, maneuvering such systems with mobile manipulators (MM) is challenging mostly due to the following reasons: 1) These systems feature nonholonomic constraints and considerably varying inertial parameters that require online identification and adaptation. 2) These systems are widely used in human-centered environments, which demand the MM to operate in potentially crowded spaces while ensuring compliance for safe physical human-robot interaction (pHRI). We propose a control framework that plans whole-body motion based on quasi-static analysis to maneuver heavy nonholonomic carts while maintaining overall compliance. We validated our approach experimentally by maneuvering a wheelchair with a bimanual mobile manipulator, the CMU ballbot. The experiments demonstrate the proposed framework is able to track desired wheelchair velocity with loads varying from 11.8 kg to 79.4 kg at a maximum linear velocity of 0.45 m/s and angular velocity of 0.3 rad/s. Furthermore, we verified that the proposed method can generate human-like motion smoothness of the wheelchair while ensuring safe interactions with the environment.

Wheelchair Maneuvering with a Single-Spherical-Wheeled Balancing Mobile Manipulator

TL;DR

This work tackles safe and efficient wheelchair assistance using a single-spherical-wheeled, dynamically balancing CMU ballbot. It offers a holistic method that combines quasi-static whole-body planning, a task-space impedance arm controller, a balancing CoM controller, online EKF-based wheel chair parameter identification, and a pushing pose optimizer plus steering controller to coordinate pushing with body lean. Key contributions include accurate velocity tracking under varying loads, online estimation of wheelchair mass and friction, and demonstrable safety and smoothness of the passenger experience in both open and cluttered environments. The approach enables compact, agile assistance in tight spaces, potentially reducing caregiver burden and expanding mobility for wheelchair users.

Abstract

In this work, we present a control framework to effectively maneuver wheelchairs with a dynamically stable mobile manipulator. Wheelchairs are a type of nonholonomic cart system, maneuvering such systems with mobile manipulators (MM) is challenging mostly due to the following reasons: 1) These systems feature nonholonomic constraints and considerably varying inertial parameters that require online identification and adaptation. 2) These systems are widely used in human-centered environments, which demand the MM to operate in potentially crowded spaces while ensuring compliance for safe physical human-robot interaction (pHRI). We propose a control framework that plans whole-body motion based on quasi-static analysis to maneuver heavy nonholonomic carts while maintaining overall compliance. We validated our approach experimentally by maneuvering a wheelchair with a bimanual mobile manipulator, the CMU ballbot. The experiments demonstrate the proposed framework is able to track desired wheelchair velocity with loads varying from 11.8 kg to 79.4 kg at a maximum linear velocity of 0.45 m/s and angular velocity of 0.3 rad/s. Furthermore, we verified that the proposed method can generate human-like motion smoothness of the wheelchair while ensuring safe interactions with the environment.
Paper Structure (17 sections, 20 equations, 10 figures)

This paper contains 17 sections, 20 equations, 10 figures.

Table of Contents

  1. INTRODUCTION
  2. Ballbot Model and Control
  3. Kinematics Model
  4. Task-space Impedance Controller
  5. Balancing Controller
  6. Wheelchair Modeling
  7. Wheelchair Dynamics
  8. Wheelchair Parameter Estimation
  9. Wheelchair Pushing Controller
  10. Pushing Pose Optimizer
  11. Steering Controller
  12. Experiments
  13. Velocity Tracking with Different Loads
  14. Motion Smoothness
  15. Physical Compliance
  16. ...and 2 more sections

Figures (10)

  • Figure 1: Time-lapse picture of the CMU ballbot maneuvering wheelchair around obstacle.
  • Figure 2: Control framework diagram. (a) The reference velocity is first generated by the path planner or sent from a gamepad. The velocity is then tracked by the wheelchair-pushing controller by computing the optimal CoM leaning angle and end-effector position commands. (b) The lower-level task-impedance arm controller then tracks the desired end-effector position commands. A wrench estimator is implemented to provide force estimation that is used for wheelchair parameter identification. (c) The CoM leaning-angle command is tracked with a balancing controller with CoM compensation.
  • Figure 3: (a) The ballbot dynamics model. (b) Quasi-static analysis with forces exerted at the end-effectors.
  • Figure 4: Planar wheelchair model. (a) Geometric notations. (b) Input force notations.
  • Figure 5: End-effector displacement during 1-minute wheelchair maneuvering experiment on (a) x axis and (b) y axis w.r.t $\{B\}$. The maximum displacement mismatch between the two EEs are 0.011m on the x-axis and 0.052m on the y-axis.
  • ...and 5 more figures