首页|Research from Huazhong University of Science and Technology Has Provided New Stu dy Findings on Robotics (Sequential Optimal Trajectory Planning Scheme for Robot ic Manipulators along Specified Path Based on Direct Collocation Method)
Research from Huazhong University of Science and Technology Has Provided New Stu dy Findings on Robotics (Sequential Optimal Trajectory Planning Scheme for Robot ic Manipulators along Specified Path Based on Direct Collocation Method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on robotics. Acc ording to news reporting out of Wuhan, People’s Republic of China, by NewsRx edi tors, research stated, “Robotic manipulators play a pivotal role in modern intel ligent manufacturing and unmanned production systems, often tasked with executin g specific paths accurately. However, the input of the robotic manipulators is t rajectory which is a path with time information.” Financial supporters for this research include The Key R&D Program of Hubei Province. The news reporters obtained a quote from the research from Huazhong University o f Science and Technology: “The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC)methods. This paper conc entrates on addressing challenges associated with the latter methods. In MPDC me thods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such a s robotic manipulators, striking a balance between solving time and path discret ization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. N onetheless, the conventional application of the MR algorithm requires solving th e original problem in each iteration; these processes are extremely time-consumi ng and may fail to solve when dealing with a complex dynamic system. As a result , we propose a sequential optimal trajectory planning scheme to solve the proble m efficiently by dividing the original optimal control (OC) problem into two sta ges: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced du ring discretization.”
Huazhong University of Science and Techn ologyWuhanPeople’s Republic of ChinaAsiaAlgorithmsEmerging Technologie sMachine LearningRoboticsRobots
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