@inproceedings {icaps16-202,
    track    = {​​Robotics Track},
    title    = {Path Planning under Interface-Based Constraints for Assistive Robotics},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13145},
    author   = {Alexander Broad and  Brenna Argall},
    abstract = {We present a heuristic-based search method for path planning in shared human-robot control scenarios in which the robot should adhere to specific motion constraints imposed by the human's control interface. This approach to path planning gives special consideration to kinematic and dynamic constraints introduced to reconcile discrepancies between the control space of the user and the control space of the robot. The resulting paths more closely mirror paths produced by users of the same interface; which is helpful, for example, when inferring human intent or for control sharing.  Our first insight is to develop a hierarchical finite state machine describing the constrained state space, state transitions and associated  costs. We then use this definition to embed the constraints of the interface into our heuristic planning algorithm, named C*, with simple modifications to the A*/D* family of graph search algorithms. This approach allows us to maintain powerful theoretical guarantees such as complexity and completeness. In this paper, we ground our augmented path planning algorithm with an implementation on a robotic wheelchair system and a Sip-and-Puff interface.  We demonstrate that the new approach produces paths and control signals that more closely resemble user-generated data and can easily be incorporated into real hardware systems.},
    keywords = {robot motion; path; task and mission planning and execution,real-world planning applications for autonomous robots,mixed-initiative planning and variable autonomy for robotic systems,human-aware planning and execution in human-robot interaction; including safety}
}

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This approach to path planning gives special consideration to kinematic and dynamic constraints introduced to reconcile discrepancies between the control space of the user and the control space of the robot. The resulting paths more closely mirror paths produced by users of the same interface; which is helpful, for example, when inferring human intent or for control sharing. Our first insight is to develop a hierarchical finite state machine describing the constrained state space, state transitions and associated costs. We then use this definition to embed the constraints of the interface into our heuristic planning algorithm, named C*, with simple modifications to the A*/D* family of graph search algorithms. This approach allows us to maintain powerful theoretical guarantees such as complexity and completeness. In this paper, we ground our augmented path planning algorithm with an implementation on a robotic wheelchair system and a Sip-and-Puff interface. 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