| Manipulator are an important part of the manufacturing industry and are widely used in various industrial fields.At present,the work of manipulator on the market is achieved by manual teaching.Although this method is very mature,it has many problems such as time-consuming and labor-intensive,low automation,and single application scenarios.In recent years,industrial products have been rapidly updated,and traditional manual teaching methods have become inadequate.Therefore,the use of manipulator motion planning to solve the problem of autonomous obstacle avoidance of manipulator has become the current research focus.As the dimensionality increases,the traditional motion planning algorithm will increase the amount of calculation exponentially,which is not suitable for manipulator with multiple degrees of freedom.The Rapid-exploration Random Tree(RRT)and its improved algorithm are a sampling-based motion planning algorithm,which does not require deterministic modeling of the surrounding environment,and judges the location of obstacles through collision detection,which is high-dimensional the problem of motion planning provides a solution.The RRT*algorithm can find an approximately optimal path,but there are problems such as a long process time,slow convergence speed,and poor quality of the obtained path.For this reason,this paper proposes a method to improve RRT*,and conducts research on motion planning and obstacle avoidance of a six-degree-of-freedom manipulator.To study the motion planning and obstacle avoidance problems of the manipulator,the establishment of the kinematics model of the manipulator is the basis.This article takes the six-degree-of-freedom tandem manipulator as the research object,uses the DH parameter method to model,and analyzes the forward motion of the six-degree-of-freedom manipulator and inverse kinematics,and use numerical methods to solve inverse kinematics,and use MATLAB’s robot toolbox to program the results to verify the correctness of the built model.In view of the long search time and low efficiency of the RRT*algorithm,a new method is introduced to find the best path.On the premise of maintaining the original characteristics of the RRT*algorithm,an optimal path can be found with less running time.The algorithm uses a goal-oriented method to expand the search tree toward the target location and uses Gaussian probability distribution for sampling,effectively avoiding the randomness of global sampling,helping to reduce the overall state search space and improving path convergence speed.In view of the poor quality of the path obtained by the RRT series algorithm,which cannot be used for actual production needs,the path obtained by the algorithm is subjected to three B-spline trajectory smoothing processing to smooth the zigzag path,thereby ensuring the smooth operation of the robotic arm.Using MATLAB to carry out a comparative experiment of autonomous obstacle avoidance of a robotic arm in a multi-obstacle environment.The experimental results show that:Compared with the original RRT*algorithm,the proposed improved algorithm shortens the final path length by 34%and reduces the search time by 42%,the planning efficiency is improved and the effectiveness of the proposed algorithm is verified. |
