Dynamic path planning over CG-Space of 10DOF Rover with static and randomly moving obstacles using RRT* rewiring

Robotica ◽  
2022 ◽  
pp. 1-20
Author(s):  
Shubhi Katiyar ◽  
Ashish Dutta
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Target Location ◽  
Dynamic Environments ◽  
Moving Obstacles ◽  
Intelligent Robots ◽  
3D Terrain ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Real Time Simulations

Abstract Dynamic path planning is a core research content for intelligent robots. This paper presents a CG-Space-based dynamic path planning and obstacle avoidance algorithm for 10 DOF wheeled mobile robot (Rover) traversing over 3D uneven terrains. CG-Space is the locus of the center of gravity location of Rover while moving on a 3D terrain. A CG-Space-based modified RRT* samples a random space tree structure. Dynamic rewiring this tree can handle the randomly moving obstacles and target in real time. Simulations demonstrate that the Rover can obtain the target location in 3D uneven dynamic environments with fixed and randomly moving obstacles.

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.

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