Research on wireless robot path planning under edge computing considering multistep searching and inflection points

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

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Downhole robot path planning based on improved D* algorithmn

2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) ◽

10.1109/icspcc50002.2020.9259532 ◽

2020 ◽

Author(s):

Tian Ma ◽

Jiahao Lv ◽

Ming Guo

Keyword(s):

Path Planning ◽

Robot Path Planning ◽

Downhole Robot ◽

Robot Path

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Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211019222 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110192

Author(s):

Songcan Zhang ◽

Jiexin Pu ◽

Yanna Si ◽

Lifan Sun

Keyword(s):

Path Planning ◽

Convergence Rate ◽

Mobile Robot ◽

Local Optimization ◽

Ant Colony ◽

Ant Colony System ◽

Geometric Features ◽

Robot Path Planning ◽

Computation Efficiency ◽

Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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