Admissible gap navigation: A new collision avoidance approach

Mujahed, Muhannad; Fischer, Dirk; Mertsching, Bärbel

doi:10.1016/j.robot.2018.02.008

Cited by 29 publications

(13 citation statements)

References 62 publications

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“…The arbitration's second stage is to adjust user input commands whenever they are deemed unsafe. A gap-based navigation method is chosen for this stage [26], where "gaps" are extracted from rangefinder data and a strategy of traversing the closest gap is applied. The concept of identifying gaps in the surroundings is particularly compelling for XSC, as it adds another semantic layer of abstraction around sensory state information.…”

Section: B Assistive Robot Navigationmentioning

confidence: 99%

Towards Explainable Shared Control using Augmented Reality

Zolotas

Demiris

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Shared control plays a pivotal role in establishing effective human-robot interactions. Traditional control-sharing methods strive to complement a human's capabilities at safely completing a task, and thereby rely on users forming a mental model of the expected robot behaviour. However, these methods can often bewilder or frustrate users whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. To resolve this model misalignment, we introduce Explainable Shared Control as a paradigm in which assistance and information feedback are jointly considered. Augmented reality is presented as an integral component of this paradigm, by visually unveiling the robot's inner workings to human operators. Explainable Shared Control is instantiated and tested for assistive navigation in a setup involving a robotic wheelchair and a Microsoft HoloLens with add-on eye tracking. Experimental results indicate that the introduced paradigm facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment.

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Section: B Assistive Robot Navigationmentioning

confidence: 99%

Towards Explainable Shared Control using Augmented Reality

Zolotas

Demiris

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…These sensors comprise the input to the Navigation Module which is composed by a collection of ROS packages. This Navigation Module uses a "gap-based" algorithm for reactive obstacle avoidance, which finds admissible gaps [24]. Furthermore, this module has the capability to receive location points towards which the smart wheelchair will autonomously navigate.…”

Section: A Smart Wheelchairmentioning

confidence: 99%

Augmented Reality Controlled Smart Wheelchair Using Dynamic Signifiers for Affordance Representation

Chacon-Quesada

Demiris

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The design of augmented reality interfaces for people with mobility impairments is a novel area with great potential, as well as multiple outstanding research challenges. In this paper we present an augmented reality user interface for controlling a smart wheelchair with a head-mounted display to provide assistance for mobility restricted people. Our motivation is to reduce the cognitive requirements needed to control a smart wheelchair. A key element of our platform is the ability to control the smart wheelchair using the concepts of affordances and signifiers. In addition to the technical details of our platform, we present a baseline study by evaluating our platform through user-trials of able-bodied individuals and two different affordances: 1) Door-Go Through and 2) People-Approach. To present these affordances to the user, we evaluated fixed symbol based signifiers versus our novel dynamic signifiers in terms of ease to understand the suggested actions and its relation with the objects. Our results show a clear preference for dynamic signifiers. In addition, we show that the task load reported by participants is lower when controlling the smart wheelchair with our augmented reality user interface compared to using the joystick, which is consistent with their qualitative answers.

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“…al. uses collision avoidance approach using gap navigation [10]. How to plan motion for dynamic obstacles with Uncertain Motion Patterns is approached by Georges S. Aoude et.…”

Section: Introductionmentioning

confidence: 99%

OperativeCriticalPointBug algorithm-local path planning of mobile robot avoiding obstacles

Das¹,

Dutta

Debnath

2020

IJEECS

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<span>For Autonomous Mobile Robot one of the biggest and interesting issues is path planning. An autonomous mobile robot should be able determine its own path to reach destination. This paper offers a new algorithm for mobile robot to plan a path in local environments with stationary as well as moving obstacles. For movable robots’ path planning OperativeCriticalPointBug (OCPB) algorithm, is a new Bug algorithm. This algorithm is carried out by the robot throughout the movement from source to goal, hence allowing the robot to rectify its way if a new obstacle comes into the route or any existing obstacle changes its route. According as, not only the robot tries to avoid clash with other obstacle but also tries a series of run time adjustment in its way to produce roughly a best possible path. During journey the robot is believed to be capable to act in an unknown location by acquiring information perceived locally. Using this algorithm the robot can avoid obstacle by considering its own as well as the obstacle’s dimension. The obstacle may be static or dynamic. The algorithm belongs to bug family.</span>

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Admissible gap navigation: A new collision avoidance approach

Cited by 29 publications

References 62 publications

Towards Explainable Shared Control using Augmented Reality

Towards Explainable Shared Control using Augmented Reality

Augmented Reality Controlled Smart Wheelchair Using Dynamic Signifiers for Affordance Representation

OperativeCriticalPointBug algorithm-local path planning of mobile robot avoiding obstacles

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