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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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November 2024
Latest News
Japanese researchers test detection devices at West Valley
Two research scientists from Japan’s Kyoto University and Kochi University of Technology visited the West Valley Demonstration Project in western New York state earlier this fall to test their novel radiation detectors, the Department of Energy’s Office of Environmental Management announced on November 19.
Junhao Zhang, Weiwei Chen, Bingyu Ni, Jing Zheng, Kaixin Zhao, Wanyi Tian, Chao Jiang
Nuclear Science and Engineering | Volume 198 | Number 8 | August 2024 | Pages 1668-1681
Research Article | doi.org/10.1080/00295639.2023.2257508
Articles are hosted by Taylor and Francis Online.
During the decommissioning process of nuclear facilities, workers are exposed to radiation and face the risk of exceeding safe dose limits. Ensuring the safety of personnel requires not only enhancing radiation protection measures but also optimizing work paths to minimize exposure time and avoid high-radiation areas. This paper proposes a nested optimization algorithm that combines an ant colony optimization (ACO) with an improved A* algorithm for the decommissioning of a nonradiation source. The algorithm aims to minimize the total radiation dose and transforms the original path optimization problem into an equivalent traveling salesperson problem. The improved A* algorithm is employed in the inner layer to calculate the path with the lowest radiation dose for any given sales order. The ACO operates in the outer layer to determine a set of optimal working paths that traverse all target points. The provided solution example demonstrates that the proposed path optimization algorithm effectively integrates the radiation field and obstacles. It successfully identifies a sequence for dismantling with the lowest dose and corresponding optimal work path while ensuring the completion of the dismantling task. These findings are expected to offer valuable insights for optimizing personnel work paths during the subsequent decommissioning process of nuclear facilities.