Wheelchair Navigation: Automatically Adapting to Evolving Environments

Fearn, Tomos; Labrosse, Frédéric; Shaw, Patricia

doi:10.1007/978-3-030-25332-5_49

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…However, when a user is onboard, this behavior is perceived as a sudden and unpredictable change in direction, potentially leading to uncomfortable and even dangerous situations, such as overturning. Consequently, significant modifications are necessary for traditional algorithms to address these issues, adding complexity to the solution [72][73][74][75][76][77]. With the increasing popularity and capabilities of deep learning methods, there has been a development of robot navigation methods utilizing neural networks.…”

Section: Related Workmentioning

confidence: 99%

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Pacini,

Dini,

Fanucci

2024

Electronics

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Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness.

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Section: Related Workmentioning

confidence: 99%

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Pacini,

Dini,

Fanucci

2024

Electronics

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“…The 'move base' ROS package utilises the LIDAR to avoid collisions whilst traversing towards a goal. Its capability to work in low-light conditions and its wider field of view compared to that of the camera makes the LIDAR a safer alternative for avoiding collisions [6].…”

Section: Data Pipelinementioning

confidence: 99%

Smart wheelchairs: Visual perception pipeline to improve prediction of user intention

Fearn¹,

Labrosse²,

Shaw³

2022

UK-RAS Conference for PhD and Early Career Researchers Proceedings

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Wheelchairs aid people with physical disabilities by assisting with mobility, thus improving their independence. Autonomous assistance on wheelchairs are limited to prototypes that provide 'smart functionality', by completing tasks such as docking or terrain adaption. The biggest constraints are navigating within dynamic environments, such as the home.This paper describes the data pipeline to automate the wheelchair navigation process, from classifying an object, estimating the user's intention via verbal command (e.g. take me to the fridge) and navigating towards a goal.Object locations will be registered within a map whilst contextual meta data is calculated. A combination of object classification confidence and object instances is used to calculate the uniqueness of all identifiable objects. Thus, assisting in predicting the user's intention. For example, if a "go to the fridge" request is received, the wheelchair will know that the fridge is located within the kitchen, and therefore drive to the kitchen and then the fridge.Results show that utilising contextual data reduces the likelihood of false-positive object detections being registered by the navigation pipeline, thus is more likely to interpret the user intention more accurately.

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“…Object segmentation and detection. [34] Stereo camera, computer, wheelchair. Image processing to follow the legs of a companion.…”

Section: Control Systems Referencesmentioning

confidence: 99%

“…This system implements a PID controller for the execution of the system and uses Kalman filters to be able to read the signals accurately. A method is implemented by Couceiro et al [34] where optic flow vectors are used, placed in the visual field of the camera located on the legs of the user. The wheelchair follows the movement the leg indicates through images captured by the camera.…”

Section: Cameras or Optical Sensors With Different Characteristicsmentioning

confidence: 99%

Control Systems and Electronic Instrumentation Applied to Autonomy in Wheelchair Mobility: The State of the Art

Callejas-Cuervo

Gonźalez-Cely

Bastos

2020

Sensors

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Automatic wheelchairs have evolved in terms of instrumentation and control, solving the mobility problems of people with physical disabilities. With this work it is intended to establish the background of the instrumentation and control methods of automatic wheelchairs and prototypes, as well as a classification in each category. To this end a search of specialised databases was carried out for articles published between 2012 and 2019. Out of these, 97 documents were selected based on the inclusion and exclusion criteria. The following categories were proposed for these articles: (a) wheelchair instrumentation and control methods, among which there are systems that implement micro-electromechanical sensors (MEMS), surface electromyography (sEMG), electrooculography (EOG), electroencephalography (EEG), and voice recognition systems; (b) wheelchair instrumentation, among which are found obstacle detection systems, artificial vision (image and video), as well as navigation systems (GPS and GSM). The results found in this review tend towards the use of EEG signals, head movements, voice commands, and algorithms to avoid obstacles. The most used techniques involve the use of a classic control and thresholding to move the wheelchair. In addition, the discussion was mainly based on the characteristics of the user and the types of control. To conclude, the articles exhibited the existing limitations and possible solutions in their designs, as well as informing the physically disabled community about the technological developments in this field.

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Wheelchair Navigation: Automatically Adapting to Evolving Environments

Cited by 5 publications

References 4 publications

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Smart wheelchairs: Visual perception pipeline to improve prediction of user intention

Control Systems and Electronic Instrumentation Applied to Autonomy in Wheelchair Mobility: The State of the Art

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