A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

Abstract

Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.

Application of an Improved Ant Colony Algorithm in Robot Path Planning and Mechanical Arm Management

“…First, the workspace of the mechanical arm is assumed to be a 2*2*1m cube with three same identical obstacles. The size simplified with the cuboid envelope method is 0.5*0.5*0.5m, and the coordinates of initial point and target point are (7, 17, 2) and (12,8,4), respectively. The grid method is used for environment modeling, and the size of grid block is set to 0.2*0.2*0.2m.…”

“…Global path planning refers to the path planning of robot after all working environments are mastered, which can also be called static path planning [7]. Local path planning based on sensor is to plan the path of robot when the working environment information is completely or partially unknown, which can also be called dynamic path planning [8]. With the increasing demand for robot cluster operation, traditional optimization methods are used to solve complex multi terminal operation problems, which become very difficult.…”

Research on Global Dynamic Path Planning Method Based on Improved A ∗ Algorithm

“…Jikai Wang et al [12] proposed a global path planning framework based on hybrid mapping, which improved the efficiency of path planning in complex environments, but it cannot guarantee the optimal path. Xiaoru Song et al [13] proposed a dynamic global path planning method suitable for mobile robots, which can plan a smooth path in a dynamic environment, but the efficiency of path planning still needs to be improved. e A * algorithm based on a grid map is suitable for global path planning.…”

A Novel Face Recognition Algorithm for Imbalanced Small Samples

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