Automated perception of safe docking locations with alignment information for assistive wheelchairs

Jain, Siddarth; Argall, Brenna

doi:10.1109/iros.2014.6943272

Cited by 23 publications

(13 citation statements)

References 15 publications

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“…To accommodate the population of individuals who find it difficult or impossible to operate a PW, several researchers have used technologies originally developed for mobile robots to create smart wheelchairs [4], [6]- [9]. A smart wheelchair (SW) typically consists of either a standard PW base to which a computer and a collection of sensors have been added, or a mobile robot base to which a seat has been attached [10].…”

Section: Introduction and Review Methodologymentioning

confidence: 99%

“…& Technology [54] Ghaziabad, India 2013 ATRII [55] Kansai, Japan 2013 Chonnam National University [56] Gwangju, S. Korea 2013 King Abdulaziz University [57] Jeddah, Saudi Arabia 2013 U. of Alabama, Huntsville [58] Alabama, USA 2013 U. of Texas, Arlington [59] Texas, USA 2013 B.M.S College of Engineering [60] Bangalore, India 2014 Kumamoto University [61] Kumamoto, Japan 2014 Saitama University [62] Saitama, Japan 2014 LURCH [63] Chennai, India 2014 Integral Rehabilitation Center [64] Orizaba, Mexico 2014 University of Kent [65] Canterbury UK 2014 University of Lorraine [8] Metz, France 2014 Uni. Politecnica delle Marche [66] Ancona, Italy 2014 University Tun Hussein Onn [67] Malaysia 2014 Northwestern University [68] Illinois, USA 2014 UMBC [69] Maryland, USA 2014 Smile Rehab [70] Greenham, UK 2015 University of Nevada, Reno [26] Nevada, USA 2015 [11] interfaces and wheelchair interfaces adapted to the user's characteristics.…”

Section: Description Locationmentioning

confidence: 99%

“…Specifically, this module estimates the users' ability level to navigate to the goal independently based on past errors, and responsiveness to prompts in order to select appropriate audio prompts to assist the users in navigation. Jain et al 2014 [68] present an algorithm for the automated detection of safe docking locations at rectangular and circular docking structures (tables, desks) with proper alignment information using 3D point cloud data. Based on geometric information, computed from depth data, the method is invariant to scene or lighting changes.…”

Section: Navigational Assistancementioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future

Leaman

2017

IEEE Trans. Human-Mach. Syst.

195

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Abstract-A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology, and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to power wheelchairs and the communities they serve. Then we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.

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Section: Introduction and Review Methodologymentioning

confidence: 99%

Section: Description Locationmentioning

confidence: 99%

Section: Navigational Assistancementioning

confidence: 99%

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A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future

Leaman

2017

IEEE Trans. Human-Mach. Syst.

195

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“…Several efforts have been made by researchers to address the path planning problem for robots docking already [13,15,[20][21][22][23][24][25][26][27]. Among them, one of the most popular approaches is the artificial potential field (APF) method.…”

Section: Introductionmentioning

confidence: 99%

Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

et al. 2018

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This paper presents an integrated approach to plan smooth path for robots docking in unknown environments with obstacles. To determine the smooth collision-free path in obstacle environment, a tree structure with heuristic expanding strategy is designed as the foundation of path planning in this approach. The tree employs 3D Dubins curves as its branches and foundation for path feasibility evaluation. For the efficiency of the tree expanding in obstacle environment, intermediate nodes and collision-free branches are determined inspired by the elastic band theory. A feasible path is chosen as the shortest series of branches that connects to the docking station after the sufficient expansion of the tree. Simulation results are presented to show the validity and feasibility of the proposed approach.

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“…Potential goals could be detected by perception methods such as recognizing objects clusters/grasps[17] or saliency maps[6] for manipulation and navigation goals such as doorways[32] and docking locations[16].…”

mentioning

confidence: 99%

Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics

Jain

Argall

2019

J. Hum.-Robot Interact.

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Effective human-robot collaboration in shared autonomy requires reasoning about the intentions of the human partner. To provide meaningful assistance, the autonomy has to first correctly predict, or infer, the intended goal of the human collaborator. In this work, we present a mathematical formulation for intent inference during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user without explicit communication. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform intent recognition. Furthermore, we introduce a user-customized optimization of this adjustable rationality to achieve user personalization. We validate our approach with a human subjects study that evaluates intent inference performance under a variety of goal scenarios and tasks. Importantly, the studies are performed using multiple control interfaces that are typically available to users in the assistive domain, which differ in the continuity and dimensionality of the issued control signals. The implications of the control interface limitations on intent inference are analyzed. The study results show that our approach in many scenarios outperforms existing solutions for intent inference in assistive teleoperation and otherwise performs comparably. Our findings demonstrate the benefit of probabilistic modeling and the incorporation of human agent behavior as goaldirected actions where the adjustable rationality model is user customized. Results further show that the underlying intent inference approach directly affects shared autonomy performance, as do control interface limitations.

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Automated perception of safe docking locations with alignment information for assistive wheelchairs

Cited by 23 publications

References 15 publications

A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future

A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future

Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics

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