首页|New Robotics Data Have Been Reported by Researchers at Beijing Jiaotong University (Multi-robot Path Planning Using Learningbased Artificial Bee Colony Algorithm)
New Robotics Data Have Been Reported by Researchers at Beijing Jiaotong University (Multi-robot Path Planning Using Learningbased Artificial Bee Colony Algorithm)
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Robotics. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Multi-robot path planning (MRPP) in continuous and known environment is studied in this paper via proposing a novel local path planning approach. To plan optimal collision-free paths for multiple robots simultaneously, a novel implementation method suitable for the meta-heuristic algorithms is devised, and an improved artificial bee colony (ABC) algorithm is developed.” Financial supporters for this research include Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China (NSFC), China Scholarship Council. Our news journalists obtained a quote from the research from Beijing Jiaotong University, “Three en- hancements to the ABC algorithm are made in this context. Firstly, to better lead the search direction, the global best individual is involved in the search equations of employed bee phase and scout bee phase. Meanwhile, to boost exploitation capability, the learning method of teaching-learning based optimization (TLBO) algorithm is incorporated into the onlooker bee phase. The proposed learning-based ABC (ABCL) algorithm is used to determine the subsequent positions for all the robots based on their current coordi- nates considering the path length, safety and planning efficiency. The experimental studies on benchmark functions show that ABCL is outstanding in solving different types of optimization problems compared against seven effective meta-heuristic algorithms. More importantly, MRPP simulation results prove that ABCL outperforms its competitors in terms of generating optimal collision-free paths and running time. Compared with the original ABC, ABCL improves these two aspects on average for all tasks by 2.1% and 12.6%, respectively.”
BeijingPeople’s Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningNano-robotRobotRoboticsBeijing Jiaotong University
NETL
NSTL
Elsevier
