Robot Navigation in a Dynamically Changing Environment

Zhidkov, V N; Kim, N.V.; Udalova, N. V.

doi:10.1109/fareastcon50210.2020.9271534

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…In shared environments with people, the uncertainty of human behavior can lead to unsafe situations for the robot during navigation [4]. Additionally, the movement of people can create obstructions, hindering sensing and impairing the robot's perception of its position in the environment.…”

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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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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Development of a methodology of estimating limits of oscillations of optical system of photoelectric rotary encoders

Королев

Chekin

2021

J. Phys.: Conf. Ser.

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The development of optical sensors with rotating mechanism requires estimation of oscillations limits. Although some work has been done on to linear encoders, there’s no information on such approaches related to rotary encoders. This manuscript proposes a computational approach of estimating oscillations limits of optical systems of rotary encoders. The approach is based on machine computations of waveforms modified by shifting position of one of discs, and rotates not around main axis of optical system. For estimations of oscillations during computations, one of optical discs translated against optical axis of optical system to perform occlusions, accounting differences in light distribution with oscillations presented. As the result of applying of methodology, computer program described and tested to calculate limits of hypothetical optical system, as well as its predictions were confirmed by comparison with behavior of real rotary encoder. This data was achieved with fewer computations, compared to classical calculation methods of light propagation. Developed methodology allows creating computer programs for estimating limits of oscillations within reasonable timeframe.

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Autonomous Robot Navigation in Crowd

Afonso

Ferreira

2022

2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Edu

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Robot Navigation in a Dynamically Changing Environment

Cited by 4 publications

References 11 publications

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

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

Development of a methodology of estimating limits of oscillations of optical system of photoelectric rotary encoders

Autonomous Robot Navigation in Crowd

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