Bionic Trajectory Planning Methods Of Robotic Manipulator Based On Improved Tau Theory

Robots are playing an important role in the industry,service,and others,which are of great significance to the national economy and people’s livelihood.Trajectory planning is one of the key issues in robot operation.The traditional trajectory planning methods of robot manipulators have been extensively investigated,however,the bionic trajectory planning methods are still needed to be studied.This thesis focuses on the bionic trajectory planning for robotic manipulators based on the Tau theory that is a kind of visual cue.The main contents of this thesis can be summarized as follows.Since the existing Tau theory-based trajectory planning methods have the deficiency of insufficient compliance of the planned trajectory and excessive computation,in Chapter 3,a higher-order intrinsic guidance movement is presented and an improved Tau jerk guidance strategy is then proposed,which makes the thirdorder derivative of the traj ectory continuous,smooth,and more compliant.Moreover,the non-physical intrinsic guidance movement is proposed firstly,and a novel Tau exponential guidance strategy is then obtained according to the convergence of exponential decay,which achieves a stable trajectory,reduced computational complexity,and less computational time consumption.The feasibility of the proposed methods is illustrated by simulation experiments.In Chapter 4,since only the point-to-point trajectories can be planned by Tau theory,a multi-point continuous trajectories planning method is presented by using a bionic optimization algorithm and Tau theory.First,an improved bacteria foraging optimization algorithm(IBFOA)is presented and multiple planning objectives are converted into cost functions,which are solved to obtain several control point locations by using the IBFOA.Then,the continuous multi-point trajectory generation can be performed by using the Tau theory-based trajectory planning methods proposed in Chapter 3.According to the simulation experiments,the effectiveness and stability of the proposed method are verified and compared with similar methods,and it is shown that the proposed method can achieve multi-point continuous trajectories planning of robotic manipulators with multiple objectives.To deal with the different starting and ending points of the repetitive movements of the robotic manipulator under complex environments,in Chapter 5,the dynamic movement primitives(DMP)method is combined with Tau theory to realize the transfer learning of the robotic manipulator trajectory.The zero-phase filtering method is utilized to suppress the acceleration fluctuation of the DMP method,and the multipoint continuous trajectory of the Tau theory generated in Chapter 4 can be then learned and reproduced.The compliant trajectories with different starting and ending points are planned at a small computational cost while maintaining the same overall motion trend of the trajectories.The rapidity and effectiveness of the scheme are also verified by simulation experiments.