Obstacle navigation during locomotion was investigated in older adults using an obstacle course paradigm under different ambient lighting conditions. Similar strategies for obstacle navigation were observed between the two age groups studied (middle-old: 75-85 years and old-old adults: 85 years and older), however old-old individuals were "less" successful at avoiding obstacles. Differences observed between the two age groups in obstacle course performance may be attributed to changes in spatial reference frames that occur with age and/or differences in perceived threat of obstacles in the travel path. Reduced lighting conditions and increasing age were also found to have significant affects on obstacle navigation.
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http://dx.doi.org/10.1016/j.gaitpost.2005.06.010 | DOI Listing |
Sensors (Basel)
December 2024
School of Mechanical Engineering and Automation, Foshan University, Foshan 528225, China.
Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Electrical Engineering Department, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.
This paper presents a comprehensive review of path planning in dynamic environments. This review covers the entire process, starting from obstacle detection techniques, through path-planning strategies, and also extending to formation control and communication styles. The review discusses the key trends, challenges, and gaps in current methods to emphasize the need for more efficient and robust algorithms that can handle complex and unpredictable dynamic environments.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science, Louisiana Tech University, 201 Mayfield Ave, Ruston, LA 71272, USA.
Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent's sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional 'olfaction-only' OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities.
View Article and Find Full Text PDFISA Trans
December 2024
Robotic Research Laboratory, Centre of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
In this paper, trajectory tracking control as the pursuit of a specific target by wheel-legged mobile robots (WLMRs) in an environment with the presence of obstacles is presented. These types of robots are designed to navigate different paths such as slippery trajectories, paths with obstacles, and other challenging paths. In addition, the robot can move its legs in different surface conditions and operate more flexibly with the help of wheels attached to the legs.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Microbiology Dr D. Y. Patil Medical College, Hospital and Research Centre, Dr D. Y. Patil Vidyapeeth (Deemed-to-be-University) Pune Maharashtra India.
Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.
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