Research On Motion Planning Of Mobile Robot Based On Improved Dragonfly Algorithm

With the rapid development of science and technology and the improvement of people's living standards,people's demand for mobile robots is getting larger and larger,and the application range is getting wider and wider.It is not only widely used in aerospace,aviation and military fields,but also used in industry,agriculture and daily life of mobile robots.Therefore,motion planning of mobile robots has become a research hotspot.The motion planning of mobile robot refers to the autonomous planning of a safe path from the initial point to the target point.This paper mainly studies the motion planning of mobile robot including path planning and trajectory planning.Firstly,the paper introduces the current situation,research background and significance,path planning research method and trajectory planning method at home and abroad.And briefly introduced the chapter arrangement of the paper.Secondly,this paper proposes three methods to improve dragonfly optimization algorithm.Dragonfly algorithm has the advantages of few parameters,simple structure,easy implementation and strong robustness,but it has the disadvantages of low convergence accuracy and poor population diversity at the end of iteration.Three improvements are proposed in this paper.Firstly,the inertia weight is improved by using quadratic function and random number generated by beta distribution to improve the convergence accuracy of the algorithm.Then the grey Wolf mechanism is added to dragonfly algorithm to improve the update mode of position vector.Grey Wolf mechanism has better local search ability,which can improve the convergence accuracy and convergence speed of the algorithm.Finally,the last elimination strategy is introduced into dragonfly algorithm.Some individuals with poor fitness values were eliminated and new individuals were introduced.This can improve the diversity of the algorithm population and the ability to jump out of the local solution.The performance of the improved dragonfly algorithm was verified by 13 benchmark functions simulation.Thirdly,the global path planning of single robot and multi-machine path planning based on dragonfly optimization algorithm are studied.The environment model is established by quadratic coordinate transformation method.The individual dimensions in the improved dragonfly algorithm correspond to the horizontal coordinates of the environment model.The values in each dimension correspond to the corresponding ordinate.Therefore,the path planning problem is transformed into the problem of finding the optimal solution of each dimension.In order to solve the path planning problem of multi-robot,the multi-species dragonfly algorithm was introduced and the coordination mechanism was added.Finally,a multi-species dragonfly algorithm based on coordination mechanism is proposed.Simulation shows that this method can be used to solve the path planning problem of multiple robots,and the path planned by the improved algorithm is shorter,and the path planning experiment of single robot is carried out based on the actual environment.Fourthly,based on the result of path planning,the trajectory planning method of mobile robot based on third-order besser function is studied under the condition of given node.In order to generate an initial point attitude constraint,target point attitude constraint and kinematic constraint,this paper proposes to determine a third-order Bessel curve with two unknowns.The trajectory length can be obtained through the planned trajectory,and the interval of the time required for the execution of the trajectory can be obtained.Then,the feasible trajectory time can be obtained from the time interval through the constraints of velocity continuity,acceleration continuity,anti-sideslip and anti-rollover.Finally,the velocity of each point can be obtained through the obtained time,and the velocity planning of each point can be realized.The space generation,porch environment and U-shaped environment of trajectory search are simulated by mobile robot.