Enhancing the Obstacle Avoidance Capabilities of a Smart Wheelchair

Adámek, Roman; Bugeja, Marvin K.; Fabri, Simon G.; Grepl, R.

doi:10.1109/me54704.2022.9983446

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Furthermore, the VFH+ algorithm eliminates the fluctuations in the steering direction that affected the VFF, thanks to a smoothing approach applied to the polar histogram itself [64][65][66][67][68][69][70][71]. However, the VFH also has its drawbacks.…”

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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