Manipulator trajectory planning based on work subspace division

Abstract

The manipulator workspace is an essential element in the field of manipulator research and is of great significance for manipulator motion planning. However, little research has been conducted on dividing the manipulator workspace into working subspaces. No precise division method has been proposed; the inverse kinematics of multiple solutions in manipulator trajectory planning may also cause abrupt joint changes, thus affecting the planned trajectory. The article proposes a working subspace division method for all ball‐wrist 6DOF(degree‐of‐freedom) manipulators that satisfy the Piper criterion to address the above problems. The kinematic model of the manipulator is established, and the Jacobi matrix of the manipulator is obtained. The space of joints of the manipulator is divided into unique domains containing only single inverse kinematic solutions by means of singular trajectory lines when the determinant of the Jacobi matrix is zero; The solution from the joint space to the workspace is achieved by a nonlinear mapping, which completes the partitioning of the work subspace, and each work subspace contains only unique inverse kinematic solutions. When trajectory planning is carried out from the independent area of a single workspace to the overlapping area of multiple workspaces, selecting the inverse kinematic solution in a single working subspace can effectively avoid abrupt changes in the joints of the manipulator and trajectory misalignment caused by numerous inverse solution selection problems and make the planned trajectory smooth and consistent with the operational requirements of each scene.

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

“…The setting of super parameters will directly affect the training process and the final performance of the model. In general, it is necessary to optimize the hyperparameters and select a group of optimal hyperparameters for the model network to improve the performance and effect of learning (Chen et al, 2021c;Liu et al, 2021e;2021f;. At the same time, under certain conditions, the larger the batchsize, the better the training effect.…”

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC

“…By the above introduction, the current control methods ( Caffaz et al, 2010 ; Xiang et al, 2015 ; Mcewen et al, 2017 ; Sun et al, 2020b ) can be presumably divided into two types, one uses errors to eliminate errors, which is represented by PID ( Duan et al, 2021 ; Tao et al, 2022a ; Yun et al, 2022b ; Sun et al, 2022 ), this method, is independent of the model, adjusts only for the control process without considering the structure and state of the system and has been used in a large number of applications in practical engineering. Another one is the modern control theory represented by sliding mode control and adaptive control ( Feng et al, 2017 ; Li B. et al, 2019 ; Jiang et al, 2019b ; Liu et al, 2022a ), these methods rely on the mathematical model of the control system, but by the error with the manufacturing and processing, we are difficult to establish an accurate mathematical model, its external interference and system parameter changes are even more difficult to predict for the AUV operating in an unknown environment, so use these methods in the engineering practice is difficult.…”

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

“…The algorithm outperforms PSO, GA, grey wolf optimization algorithms (GWO). It is widely used other search algorithms on uni-modal and multi-modal test functions and is widely used in problems such as path planning for mobile robots ( Liu et al, 2022b ), control of photovoltaic microgrids ( Yuan et al, 2021 ) and optimization of battery stack model parameters ( Liu Y et al, 2022 ). We find that SSA is suitable for time-optimal trajectory planning problems and improves the initial population generation in the original algorithm through the Tent chaotic mapping method.…”