Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.
The robot’s arm trajectory can be used to improve the quality of the robot work. The current method utilizes the searching ability of the Alopex algorithm to perform a rough search of the robot’s path of action to achieve the trajectory planning of its arm, but the planning efficiency is low. To this end, this paper uses chaos control to plan the robot arm trajectory. Based on the description of the robot arm trajectory planning, the robotic arm trajectory planning is completed by using the obstacle to the intersection time, probability prediction, motion planning strategy and real-time obstacle avoidance. Experiments show that this method can carry on the efficient planning to the robot arm trajectory.
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