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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
Christian Mandel, Thomas Röfer, Udo Frese
Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
Motivation
• control of smart wheelchairs for people suffering from quadriplegia asks for
suitable interface methods
• available approaches employ still functioning communication channels like
- direction of gaze
 computer vision
[Canzler et al. 2004]
 electro-oculographic potential (EOG)
[Gips 1998, Yanco 1998]
- voice
 coarse qualitative route descriptions
[Mandel et al. 2006]
- head posture
 ultrasonic sensors
[Jaffe 1982, Ford 1995]
 inertial measurement units (IMU)
[Chen 2003]
- brain computer interfaces (BCI)
 dependent / independent BCIs
[Vanacker ???, Millan 2006]
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
Preliminary Work: Human Robot Interfaces
• interpretation of coarse qualitative route descriptions via the mapping of
pairs of spatial relations and landmarks onto annotated route graphs
along the blue path
front-left of me
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
Prerequisite: Low Level Safety Layer
• for each combination of (discretized) rotational velocity (w) and translational
velocity (v) precompute virtual sensor vs(v,w)
• at runtime vs(v,w) allows for fast collision
detection within local obstacle map
• safety layer intervenes by setting v and w to
zero if obstacle is located within dangerous part
of virtual sensor
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Head-Joystick: Basic Idea
• three degrees of freedom orientation tracker, mounted at the back
of the user`s head serves as suitable controlling equipment for an
automated wheelchair
• pitch-axis controls translational speed (v)
roll-axis controls rotational speed (w)
free yaw-axis allows user to look around
• proportional control:
doubling the head`s pitch or roll angle doubles
v or w respectively
• safety layer monitors sorrounding obstacles and
prevents from collisions
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Head-Joystick: Calibration
• calibration determines minimal and maximal
pitch and roll deflection of a particular user`s head
• dead zone around head`s point of rest defines
interval of disregarded head movements
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Head-Joystick:
Static vs. Dynamic Roll Dead Zone

• linear, quadratic, or cubic mapping of head posture
onto translational and rotational velocity
• static roll dead zone constrains rotational velocity to
zero if the head`s roll angle is within a static
intervall around the head`s rest position

• dynamic roll dead zone increases
increasing head`s pitch angle
 with
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
Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Head-Joystick: Experimental Evaluation
standard joystick
111 ms
Safety Layer Interventions
head-joystick
(static roll dead zone)
445 ms
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head-joystick
(dynamic roll dead zone)
94 ms
Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Head-Joystick: Experimental Evaluation
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Path Planner Interface: Basic Idea
• assumption 1: fixed height of user`s head
• assumption 2: level surface
• line of sight intersects with surface and
determines target position
• orientation in target position is given
by obstacle situation
• local path planner computes obstacle
free paths to target pose by means of
cubic Bezier curves
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Path Planner Interface:
Mapping Head Posture to Target Pose
• line of sight is given by
- assumed eyepoint e
- head`s pitch angle 
- head`s yaw angle 
• problem of drifting delta between head`s
yaw angle and odometry heading
• orientation in target position t is given by
tangent in t to actual distance grid, pointing
away from wheelchair
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Path Planner Interface: Cubic Bezier Curve Search Space
• p0 and p1 predetermined by current pose
and targeted pose
• p1 and p2 span search space of possible
solution paths
• computational payload example:
4400 curves
x 100 curve points
x 132 contour points
= O(58*106) collision tests
• need for efficient collision test
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
IMU based Path Planner Interface: Proof of Concept
• estimated path of an approximately 127m long test run
triggered by head posture dependent target selection
and simple voice commands
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Applying a 3DOF Orientation Tracker (IMU) as a
Human-Robot Interface for Autonomous Wheelchairs
References
• [Canzler 2004] „Person-adaptive facial feature analysis for an advanced wheelchair user-interface“ in Proceedings of the IEEE Intl. Conf. on
Mechatronics and Robotics, 2004
• [Gips 1998] „On building intelligence into eagleeyes“ in Lecture Notes in AI: Assistive Technology and Artificial Intelligence, 1998
• [Yanco 1998] „Wheelesley, a robotic wheelchair system: Indoor navigation and user interface“ in Lecture Notes in AI: Assistive Technology and
Artificial Intelligence, 1998
• [Mandel 2006] „Robot navigation based on the mapping of coarse qualitative route descriptions to route graphs“ in Proceedings of the IEEE Intl. Conf.
on Intelligent Robots and Systems, 2006
• [Jaffe 1982] „An ultrasonic head position interface for wheelchair control“ in Journal of Medical Systems, 1982
• [Ford 1995] „Ultrasonic head controller for powered wheelchairs“ in Journal of Rehabilitation Research and Development, 1995
• [Chen 2003] „A head oriented wheelchair for people with disabilities“ in Disability and Rehabilitation, 2003
• [Vanacker ???] „Context-based filtering for assisted brain-actuated wheelchair driving“, ???
• [Millan 2006] „Non-invasive brain-actuated control of a mobile robot by human eeg“ in IMIA Yearbook of Medical Informatics, 2006
Thank You!
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