A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple constraints.The inverse kinemati...A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple constraints.The inverse kinematic solution is obtained through an analytical method,and the trajectory is planned in joint space.As such,the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical,obstacle and task constraints,and the optimization variables are significantly reduced.Then teaching-learning-based optimization(TLBO)algorithm is employed to search for the redundant parameters to generate an optimal trajectory.Simulation and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the manipulator.Moreover,the planned trajectory has no theoretical end-effector deviation for the task constraint.This approach can provide a reference for the motion planning of other redundant manipulators.展开更多
The redundant humanoid manipulator has characteristics of multiple degrees of freedom and complex joint structure, and it is not easy to obtain its inverse kinematics solution. The inverse kinematics problem of a huma...The redundant humanoid manipulator has characteristics of multiple degrees of freedom and complex joint structure, and it is not easy to obtain its inverse kinematics solution. The inverse kinematics problem of a humanoid manipulator can be formulated as an equivalent minimization problem, and thus it can be solved using some numerical optimization methods. Biogeography-based optimization （BBO） is a new biogeography inspired optimization algorithm, and it can be adopted to solve the inverse kinematics problem of a humanoid manipulator. The standard BBO algorithm that uses traditional migration and mutation operators suffers from slow convergence and prematurity. A hybrid biogeography-based optimization （HBBO） algorithm, which is based on BBO and differential evolution （DE）, is presented. In this hybrid algorithm, new habitats in the ecosystem are produced through a hybrid migration operator, that is, the BBO migration strategy and Did/best/I/bin differential strategy, to alleviate slow convergence at the later evolution stage of the algorithm. In addition, a Gaussian mutation operator is adopted to enhance the exploration ability and improve the diversity of the population. Based on these, an 8-DOF （degree of freedom） redundant humanoid manipulator is employed as an example. The end-effector error （position and orientation） and the ＇away limitation level＇ value of the 8-DOF humanoid manipulator constitute the fitness function of HBBO. The proposed HBBO algorithm has been used to solve the inverse kinematics problem of the 8-DOF redundant humanoid manipulator. Numerical simulation results demonstrate the effectiveness of this method.展开更多
基金supported by Jiangsu(Industry Foresight and Key Core Technology)Key Research and Development Project(BE2022137)the National Natural Science Foundation of China(51675358).
文摘A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple constraints.The inverse kinematic solution is obtained through an analytical method,and the trajectory is planned in joint space.As such,the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical,obstacle and task constraints,and the optimization variables are significantly reduced.Then teaching-learning-based optimization(TLBO)algorithm is employed to search for the redundant parameters to generate an optimal trajectory.Simulation and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the manipulator.Moreover,the planned trajectory has no theoretical end-effector deviation for the task constraint.This approach can provide a reference for the motion planning of other redundant manipulators.
基金Project supported by the National Natural Science Foundation of China （No. 61273340） and the China Postdoctoral Science Foundation （No. 2013M541721）
文摘The redundant humanoid manipulator has characteristics of multiple degrees of freedom and complex joint structure, and it is not easy to obtain its inverse kinematics solution. The inverse kinematics problem of a humanoid manipulator can be formulated as an equivalent minimization problem, and thus it can be solved using some numerical optimization methods. Biogeography-based optimization （BBO） is a new biogeography inspired optimization algorithm, and it can be adopted to solve the inverse kinematics problem of a humanoid manipulator. The standard BBO algorithm that uses traditional migration and mutation operators suffers from slow convergence and prematurity. A hybrid biogeography-based optimization （HBBO） algorithm, which is based on BBO and differential evolution （DE）, is presented. In this hybrid algorithm, new habitats in the ecosystem are produced through a hybrid migration operator, that is, the BBO migration strategy and Did/best/I/bin differential strategy, to alleviate slow convergence at the later evolution stage of the algorithm. In addition, a Gaussian mutation operator is adopted to enhance the exploration ability and improve the diversity of the population. Based on these, an 8-DOF （degree of freedom） redundant humanoid manipulator is employed as an example. The end-effector error （position and orientation） and the ＇away limitation level＇ value of the 8-DOF humanoid manipulator constitute the fitness function of HBBO. The proposed HBBO algorithm has been used to solve the inverse kinematics problem of the 8-DOF redundant humanoid manipulator. Numerical simulation results demonstrate the effectiveness of this method.
