Modeling and Design of a Stair Climbing Wheelchair with Pose Estimation and Adjustment

Sharma, Bibhu; Pillai, Branesh M.; Borvorntanajanya, Korn; Suthakorn, Jackrit

doi:10.1007/s10846-022-01765-3

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…To secure the system stability, over half of the wheel center must be located on the upper stair, and it is expressed as (2). Moreover, the center distance of two wheels, which are in contact with stairs, should be smaller than the stair width for stability and is expressed in (3).…”

Section: B Design Parameters Of Wheel Clustermentioning

confidence: 99%

“…The slope of the stairs is measured using a sensor, and the angle of the passenger seat is separately adjusted according to the slope of the stairs. An electric wheelchair capable of moving alone on a track without wheels has also been developed [2], [3]. Depending on the angle of the stairs or the posture of approaching/ escaping the stairs, the angle of the track was adjusted using an auxiliary wheel, or the articulated track was used to secure stable friction between the stairs and the track.…”

Section: Introductionmentioning

confidence: 99%

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Design of Stair-Climbing Electric Wheelchair With Tri-Spoke Wheel and Supporting Leg

Cho,

Kim,

Park

et al. 2024

IEEE Access

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Stair climbing is necessary to improve the convenience of electric wheelchairs. An important issue in developing a climbable wheelchair is stability and speed during climbing. In this work, tri-wheel and supporting leg were adopted in the stair climbing method for high speed and stable posture. Based on the mechanical and static analyses of the climbing mechanism, the design method of the wheel cluster system was expressed as equations. These equations were proposed considering the design parameters that ensure the continuity and stability of the climbing motion. Moreover, the collision information between the stairs and the cluster system during the climbing motion was included in those equations. A validation test of the stair climbing mechanism was performed by manufacturing a prototype of 750 (W ) x 800(D) x 1,000(H) mm 3 and 120 kg. The experimental verification proved that the electric wheelchair can stably go up and down the stairs under extreme conditions (stair width = 300 mm, stair height = 180 mm) at a speed of 10 steps/min.

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Section: B Design Parameters Of Wheel Clustermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Stair-Climbing Electric Wheelchair With Tri-Spoke Wheel and Supporting Leg

Cho,

Kim,

Park

et al. 2024

IEEE Access

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“…In light of this problem, several studies have addressed solutions focused on the development of intelligent wheelchairs capable of autonomously and safely navigating shared environments with humans [1][2][3]. Works related to the integration of robots into everyday human activities, such as airports, museums, and shopping malls, demonstrate that navigation in this type of environment is a particularly challenging task.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation of Wheelchairs in Indoor Environments using Deep Reinforcement Learning and Computer Vision

Afonso,

Ferreira

2023

Preprint

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We introduce an innovative approach to autonomous navigation for motorized wheelchairs in shared indoor spaces with human presence, combining Deep Reinforcement Learning and Computer Vision. The central aim of this study is to enhance the well-being of individuals with disabilities who rely on such assistance for their mobility needs. Our methodology merges the Deep Deterministic Policy Gradient (DDPG) algorithm with advanced computer vision techniques, empowering motorized wheelchairs to navigate through environments that encompass both stationary and moving people. Comparative tests were conducted between the DDPG and Deep Q-Network (DQN) algorithms, spanning four distinct stages. Each stage represented two different scenarios from the training environment, characterized by complexity levels exceeding those to which the robot had been trained. The DDPG algorithm showcased its superior efficiency and stability compared to DQN. Across all analyzed stages, DDPG exhibited higher average success rates: 98\% (Stage 01), 89\% (Stage 02), 86\% (Stage 03), and 86\% (Stage 04), demonstrating exceptional generalization capabilities and consistently outperforming the training environment in diverse settings. In contrast, DQN struggled to avoid collisions, resulting in significantly lower average success rates: 3\% (Stage 02), 14\% (Stage 03), and 29\% (Stage 04). Our findings underscore the promising potential of our proposed solution, thereby contributing to the progress of research in this particular domain.

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Advancing Mobility in Stair Climbing with BART LAB's Intelligent Wheelchair: A Deep Learning Approach to Pose Estimation

Pillai,

Sivaraman,

Ongwattanakul

et al. 2024

2024 17th International Convention on Rehabilitation Engineering and Assistive Technology (I-CREATe)

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Modeling and Design of a Stair Climbing Wheelchair with Pose Estimation and Adjustment

Cited by 5 publications

References 54 publications

Design of Stair-Climbing Electric Wheelchair With Tri-Spoke Wheel and Supporting Leg

Design of Stair-Climbing Electric Wheelchair With Tri-Spoke Wheel and Supporting Leg

Autonomous Navigation of Wheelchairs in Indoor Environments using Deep Reinforcement Learning and Computer Vision

Advancing Mobility in Stair Climbing with BART LAB's Intelligent Wheelchair: A Deep Learning Approach to Pose Estimation

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