scholarly journals A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15140-15151 ◽  
Author(s):  
Jianya Yuan ◽  
Hongjian Wang ◽  
Changjian Lin ◽  
Dawei Liu ◽  
Dan Yu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Network Model ◽  
Dynamic Path Planning ◽  
Dynamic Path

Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 717-720 ◽  
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Probability Function ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

This paper presents a dynamic path planning method based on improved ant colony algorithm. In order to increasing the algorithm’s convergence speed and avoiding to fall into local optimum, we propose adaptive migratory probability function and updating the pheromone. We apply the improved algorithm to path planning for mobile robot and the simulation experiment proved that improved algorithm is viable and efficient.

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

2018 ◽  
Vol 13 (6) ◽  
pp. 1032-1046 ◽  
Author(s):  
Xiaoru Song ◽  
Song Gao ◽  
C.B. Chen ◽  
Kai Cao ◽  
Jiaoru Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Dynamic Obstacle

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.

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