首页|Taiyuan University of Technology Researchers Add New Findings in the Area of Rob otics (Path planning of wheeled coal mine rescue robot based on improved A* and potential field algorithm)
Taiyuan University of Technology Researchers Add New Findings in the Area of Rob otics (Path planning of wheeled coal mine rescue robot based on improved A* and potential field algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on robotics have been published . According to news reporting originating from Taiyuan, People’s Republic of Chi na, by NewsRx correspondents, research stated, “Coal mine rescue robots perform search and rescue tasks in unstructured underground tunnel environments. Traditi onal path planning algorithms may encounter issues such as low efficiency, non-o ptimal paths, and poor smoothness when applied to search spaces that are large o r complex.” Our news journalists obtained a quote from the research from Taiyuan University of Technology: “Additionally, tunnels feature complex environmental characterist ics such as intersections, where robots are prone to deviating from preset route s or scraping against tunnel walls. To address these challenges and enhance the navigation accuracy of robots, improvements to the path planning algorithm for w heeled coal mine rescue robots are proposed: The heuristic global path planning A* algorithm is enhanced by employing layered neighborhood search and pruning te chniques to optimize the search process. The cost function is refined to better balance the influence of actual cost and heuristic cost, thus more accurately as sessing the cost of each node, adapting to real situations, reducing computation al complexity, and smoothing the path using B-spline methods. The Random Sample Consensus (RANSAC) fitting algorithm is utilized to construct a geometric model of coal mine tunnel walls, facilitating the extraction of feature point coordina tes of intersections for inclusion in the planning system. The path is optimized using the local support property of B-spline basis functions. When additional p ath optimization points are added subsequently, only the shape of the curve in t he corresponding interval is affected, leaving the rest of the path unaffected. A comprehensive local force field is established based on the constructed enviro nmental geometric model and extracted feature points. Adjustment coefficients ar e introduced to optimize the distribution of the force field, and motion control is achieved using the Particle Swarm Optimization (PSO) optimized PID (Proporti on Integral Differential) algorithm, enhancing the robot’s adaptability to compl ex environments such as tunnel intersections.”
Taiyuan University of TechnologyTaiyua nPeople’s Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningNano-robotRobotRobotics
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