首页|Nantong University Researcher Broadens Understanding of Robotics (An Adaptive an d Automatic Power Supply Distribution System with Active Landmarks for Autonomou s Mobile Robots)

Nantong University Researcher Broadens Understanding of Robotics (An Adaptive an d Automatic Power Supply Distribution System with Active Landmarks for Autonomou s Mobile Robots)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on robotics have been published. According to news originating from Nantong, People’s Republic o f China, by NewsRx correspondents, research stated, “With the development of aut omation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor s hortages.” The news correspondents obtained a quote from the research from Nantong Universi ty: “However, conventional industrial mobile robots’ route-tracking algorithms t ypically rely on passive markers, leading to issues such as inflexibility in cha nging routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks-battery-powered luminous la ndmarks that enable robots to recognize and adapt to flexible navigation require ments. However, the reliance on batteries necessitates frequent recharging, prom pting the development of an automatic power supply system. This system integrate s omnidirectional contact electrodes on mobile robots, allowing to recharge acti ve landmarks without precise positional alignment. Despite these advancements, c hallenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues , this research focuses on miniaturizing active landmarks and optimizing power d istribution among landmarks.”

Nantong UniversityNantongPeople’s Re public of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobo tics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.16)