首页|Chaohu University Researcher Publishes Findings in Robotics (Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm)

Chaohu University Researcher Publishes Findings in Robotics (Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm)

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Researchers detail new data in robotics. According to news reporting out of Chaohu University by NewsRx editors, research stated, "In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path." Funders for this research include Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province; University Natural Science Research Project of Anhui Province; China University Students' Innovation And Entrepreneurship Project. Our news correspondents obtained a quote from the research from Chaohu University: "To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest."

Chaohu UniversityAlgorithmsEmerging TechnologiesMachine LearningParticle Swarm OptimizationRobotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.28)
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