Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces

Lopes, Ana Cristina Macário; Nunes, Urbano; Vaz, Luis

doi:10.1109/iembs.2010.5626221

Cited by 17 publications

(12 citation statements)

References 7 publications

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“…We observe that since K h = 1 − K R , there is only one free parameter in this formulation. This linear arbitration model has enjoyed wide adoption in the assistive wheelchair community ( [5], [27], [17], [26], [30], [19], [25], [15]). Outside of the wheelchair community, shared control path planning researchers have widely adopted Equation II.1 as a de-facto standard protocol, as extensively argued in [9], [8] (in [9], it is argued that "linear policy blending can act as a common lens across a wide range of literature").…”

Section: Related Workmentioning

confidence: 99%

Assistive Planning in Complex, Dynamic Environments: A Probabilistic Approach

Trautman

2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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We explore the probabilistic foundations of shared control in complex dynamic environments. In order to do this, we formulate shared control as a random process and describe the joint distribution that governs its behavior. For tractability, we model the relationships between the operator, autonomy, and crowd as an undirected graphical model. Further, we introduce an interaction function between the operator and the robot, that we call "agreeability"; in combination with the methods developed in [23], we extend a cooperative collision avoidance autonomy to shared control. We therefore quantify the notion of simultaneously optimizing over agreeability (between the operator and autonomy), and safety and efficiency in crowded environments. We show that for a particular form of interaction function between the autonomy and the operator, linear blending is recovered exactly. Additionally, to recover linear blending, unimodal restrictions must be placed on the models describing the operator and the autonomy. In turn, these restrictions raise questions about the flexibility and applicability of the linear blending framework. Additionally, we present an extension of linear blending called "operator biased linear trajectory blending" (which formalizes some recent approaches in linear blending such as [9]) and show that not only is this also a restrictive special case of our probabilistic approach, but more importantly, is statistically unsound, and thus, mathematically, unsuitable for implementation. Instead, we suggest a statistically principled approach that guarantees data is used in a consistent manner, and show how this alternative approach converges to the full probabilistic framework. We conclude by proving that, in general, linear blending is suboptimal with respect to the joint metric of agreeability, safety, and efficiency.

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Section: Related Workmentioning

confidence: 99%

Assistive Planning in Complex, Dynamic Environments: A Probabilistic Approach

Trautman

2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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“…9 A fuzzy shared-control also was proposed for an assistive navigation architecture based on sparse and discrete human-machine interface. 10 Similar P300/BCI designing philosophies were tested in a HOAP2 humanoid robot with a bit rate of up to 24 bits/min with an accuracy of 95% over four choices. 11 The same humanoid robot learned some movements (such as walking, grasping) through self-paced mental imagery BCI and, thereafter, the user could send high-level commands using P300 so that the robot repeated the learned movements.…”

Section: Introductionmentioning

confidence: 99%

“…16 All of these projects made use of mental tasks, motor imagery or P300 for controlling the robotic systems. [3][4][5][6][7][8][9][10][11][12][13][14][15][16] However, such systems generally require a training step that can necessitate from some minutes up to hours or even days. In addition, the user is sometimes subject to mental efforts that could produce mental fatigue.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot navigation with a self-paced brain–computer interface based on high-frequency SSVEP

et al. 2013

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SUMMARYA brain–computer interface (BCI) is a system for commanding a device by means of brain signals without having to move any muscle. One kind of BCI is based on Steady-State Visual Evoked Potentials (SSVEP), which are evoked visual cortex responses elicited by a twinkling light source. Stimuli can produce visual fatigue; however, it has been well established that high-frequency SSVEP (>30 Hz) does not. In this paper, a mobile robot is remotely navigated into an office environment by means of an asynchronous high-frequency SSVEP-based BCI along with the image of a video camera. This BCI uses only three electroencephalographic channels and a simple processing signal method. The robot velocity control and the avoidance obstacle algorithms are also herein described. Seven volunteers were able to drive the mobile robot towards two different places. They had to evade desks and shelves, pass through a doorway and navigate in a corridor. The system was designed so as to allow the subject to move about without restrictions, since he/she had full robot movement's control. It was concluded that the developed system allows for remote mobile robot navigation in real indoor environments using brain signals. The proposed system is easy to use and does not require any special training. The user's visual fatigue is reduced because high-frequency stimulation is employed and, furthermore, the user gazes at the stimulus only when a command must be sent to the robot.

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“…For instance, any type of switching control (arguably the most broadly deployed type of shared control; for example, anti-lock braking systems in cars [4] and autopilot in commercial avionics [26] are switching control systems), where either the human or machine is in complete control of the platform at time t, is just linear blending where K h , K R = 0, 1. Linear blending has enjoyed wide adoption in the assistive wheelchair community [10], [58], [38], [57], [61], [47], [56], [35]. In [48], [59], an even broader adoption of linear blending is advocated.…”

Section: Related Workmentioning

confidence: 99%

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

Trautman

2017

Robotics: Science and Systems XIII

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Abstract-Human machine teaming has, for decades, been conceptualized as a function allocation (FA) or levels of autonomy (LOA) process: the human is suited for some tasks, while the machine is suitable for others, and as machines improve they take over duties previously assigned to humans. A wide variety of methods-including adaptive, adjustable, blended, supervisory and mixed initiative control, implemented discretely or continuously, as potential fields, as virtual fixture interfaces, or haptic interfaces-are derivatives of FA/LOA. We formalize FA/LOA (and all their derivatives) under a single mathematical formulation called classical shared control (CSC). Despite the widespread adoption of CSC, we prove that it fails to optimize human and robot agreement and intent if either the human or robot model displays "intention ambiguity" (e.g., the human's intended goal is unclear or the robot finds multiple viable solutions). Practically, this suboptimality can manifest as unnecessary and unresolvable disagreement (an unnecessary deadlock). For instance, if the robot chooses to go left around an obstacle and the human chooses to go right, CSC only provides two solutions: freeze in place or collide with the obstacle (we provide a wide variety of failure examples in [52], https://arxiv.org/abs/1611.09490). We find that CSC suboptimality stems from arbitrating over model samples, rather than over models. Our key insight is thus to arbitrate over human and robot distributions; we prove this method optimizes human and robot agreement and intent and resolves deadlocking. Our key contribution is computationally efficient distribution arbitration: if the human and robot carry N our joint has fewer modes than the individual agent models. We call our approach N min -sparse generalized shared control.

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Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces

Cited by 17 publications

References 7 publications

Assistive Planning in Complex, Dynamic Environments: A Probabilistic Approach

Assistive Planning in Complex, Dynamic Environments: A Probabilistic Approach

Mobile robot navigation with a self-paced brain–computer interface based on high-frequency SSVEP

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

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