Inicio  /  Applied Sciences  /  Vol: 13 Par: 7 (2023)  /  Artículo
ARTÍCULO
TITULO

Patient?Robot Co-Navigation of Crowded Hospital Environments

Krishna Kodur and Maria Kyrarini    

Resumen

Intelligent multi-purpose robotic assistants have the potential to assist nurses with a variety of non-critical tasks, such as object fetching, disinfecting areas, or supporting patient care. This paper focuses on enabling a multi-purpose robot to guide patients while walking. The proposed robotic framework aims at enabling a robot to learn how to navigate a crowded hospital environment while maintaining contact with the patient. Two deep reinforcement learning models are developed; the first model considers only dynamic obstacles (e.g., humans), while the second model considers static and dynamic obstacles in the environment. The models output the robot?s velocity based on the following inputs; the patient?s gait velocity, which is computed based on a leg detection method, spatial and temporal information from the environment, the humans in the scene, and the robot. The proposed models demonstrate promising results. Finally, the model that considers both static and dynamic obstacles is successfully deployed in the Gazebo simulation environment.

 Artículos similares

       
 
Jesus M. Torres Palenzuela, Luis González Vilas, Francisco M. Bellas, Elina Garet, África González-Fernández and Evangelos Spyrakos    
The NW coast of the Iberian Peninsula is dominated by extensive shellfish farming, which places this region as a world leader in mussel production. Harmful algal blooms in the area frequent lead to lengthy harvesting closures threatening food security. T... ver más
Revista: Water

 
Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika    
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is ... ver más
Revista: Information

 
Aniket Kumar Singh, Bishal Lamichhane, Suman Devkota, Uttam Dhakal and Chandra Dhakal    
This study investigates self-assessment tendencies in Large Language Models (LLMs), examining if patterns resemble human cognitive biases like the Dunning?Kruger effect. LLMs, including GPT, BARD, Claude, and LLaMA, are evaluated using confidence scores ... ver más
Revista: Information

 
Xuanshuo Shi, Zhongfeng Qiu, Yunjian Hu, Dongzhi Zhao, Aibo Zhao, Hui Lin, Yating Zhan, Yu Wang and Yuanzhi Zhang    
Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin... ver más
Revista: Water

 
Somayeh Shahrabadi, Telmo Adão, Emanuel Peres, Raul Morais, Luís G. Magalhães and Victor Alves    
The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computatio... ver más
Revista: Algorithms