首页|New Robotics Study Findings Recently Were Reported by Researchers at King’s Coll ege London (Path Planning Optimization Based Interference Awareness for Mobile R obots In Mmwave Multi Cell Networks)
New Robotics Study Findings Recently Were Reported by Researchers at King’s Coll ege London (Path Planning Optimization Based Interference Awareness for Mobile R obots In Mmwave Multi Cell Networks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics are disc ussed in a new report. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, “The emerging beyond fifth-gen eration (B5G) and envisioned sixth-generation (6G) wireless networks are conside red as key enablers in supporting a diversified set of applications for industri al mobile robots (MRs). The scenario under investigation in this paper relates t o mobile robots that autonomously roam on an industrial floor and perform a vari ety of tasks at different locations, whilst utilizing high directivity beamforme rs in millimeter wave (mmWave) small cells.” The news reporters obtained a quote from the research from King’s College London , “In such scenarios, the potential close proximity of mobile robots connected t o different base stations may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate in the network. To re solve this issue, a novel MR optimal path planning scheme via a mixed integer pr ogramming formulation is proposed where robots’ trajectory is considered jointly with the interference level at different beam sectors. To combat the curse of d imensionality, a geographical division clustering based MR path planning heurist ic scheme is proposed to enable scalability and real-time decision making. The p roposed heuristic aims to find a low interference path for each mobile robot whi lst achieving a near-optimal performance.”
LondonUnited KingdomEuropeEmerging TechnologiesMachine LearningNano-robotRoboticsKing’s College London
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