Collision Anticipation via Deep Reinforcement Learning for Visual Navigation

Gutiérrez-Maestro, Eduardo; López-Sastre, Roberto J.; Maldonado-Bascón, Saturnino

doi:10.1007/978-3-030-31332-6_34

Pattern Recognition and Image Analysis

2019

DOI: 10.1007/978-3-030-31332-6_34

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Collision Anticipation via Deep Reinforcement Learning for Visual Navigation

Eduardo Gutiérrez-Maestro

Roberto J. López-Sastre

Saturnino Maldonado-Bascón

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Cited by 2 publications

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“…Moreover, and in order to increase the efficiency of autonomous navigation, deep learning techniques can be applied. In local navigation plans, they can be used to avoid moving objects by means of deep reinforcement learning (DRL) [39], using visual information provided by LiDAR (light detection and ranging) or a depth camera [40,41], or by fusion of sensory information [21,42,43].…”

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confidence: 99%

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair

García

Marrón

Melino

et al. 2023

IJERPH

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Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users’ needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms.

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confidence: 99%

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair

García

Marrón

Melino

et al. 2023

IJERPH

View full text Add to dashboard Cite

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Multigoal Visual Navigation With Collision Avoidance via Deep Reinforcement Learning

Xiao

Yuan

et al. 2022

IEEE Trans. Instrum. Meas.

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Collision Anticipation via Deep Reinforcement Learning for Visual Navigation

Cited by 2 publications

References 9 publications

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair

Multigoal Visual Navigation With Collision Avoidance via Deep Reinforcement Learning

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