首页|Data from Shanghai Ocean University Broaden Understanding of Robotics (A novel parallel ant colony optimization algorithm for mobile robot path planning)
Data from Shanghai Ocean University Broaden Understanding of Robotics (A novel parallel ant colony optimization algorithm for mobile robot path planning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in robotics. According to news reporting from Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research.” The news correspondents obtained a quote from the research from Shanghai Ocean University: “This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out.”
Shanghai Ocean UniversityShanghaiPeople’s Republic of ChinaAsiaAlgorithmsAnt Colony OptimizationEmerging TechnologiesMachine LearningRobotRobotics
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