首页|New Robotics Study Findings Have Been Reported from Chongqing University (Time-jerk Optimal Trajectory Planning for Industrial Robots With Coupled Interpolation Function Selection)
New Robotics Study Findings Have Been Reported from Chongqing University (Time-jerk Optimal Trajectory Planning for Industrial Robots With Coupled Interpolation Function Selection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics are discussed in a new report. According to news reporting out of Chongqing, People’s Republic of China, by NewsRx editors, research stated, “In the con- temporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently over- looks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes.” Funders for this research include The presented work was supported by the National Key Research and Development Project of China, National Key Research and Development Project of China, National Natural Science Foundation of China (NSFC), Natural Science Foundation of Chongqing, Innovation Group Science Fund of Chongqing Natural Science Foundation, Self-Planned Task of State Key Laboratory of Mechanical Transmission. Our news journalists obtained a quote from the research from Chongqing University, “To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition-Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time-jerk optimal trajectory.”
ChongqingPeople’s Republic of ChinaAsiaEmerging Tech- nologiesMachine LearningNano-robotRoboticsChongqing University
NETL
NSTL
Wiley
