Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on robotics have been presented. Ac cording to news reporting from Chongqing, People's Republic of China, by NewsRx journalists, research stated, "The complexity of the environment limits the accu racy of the traditional Adaptive Monte Carlo Localization(AMCL) algorithm, which also suffers from high computational effort and particle degradation due to las er model limitations. To address these issues, an optimized AMCL algorithm with a bounding box is proposed." Funders for this research include Wanzhou District Science And Technology Bureau ; General Project of The Natural Science Foundation of Chongqing Science And Tec hnology Commission. Our news reporters obtained a quote from the research from Chongqing Three Gorge s University: "The AMCL algorithm is first parameterized and initialized to the particle swarm. During the particle iteration process, collision detection is pe rformed on the bounding box. If a collision occurs, the particle filter is not u pdated and its particle weight is set to 1. If there is no collision, the partic le filter is updated normally and the particle weight is set to 0. Then, the par ticles are resampled and updated based on the measurement data and motion model. After experimental verification, this method's self-localization trajectory is closer to the actual path, and the measurement error fluctuation is smaller."
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