This study aims to propose a method for collision avoidance and hovering, enabling robots to perform efficient and safe inspection tasks in complex inlet environments, while also having capabilities for collision avoidance and trajectory planning. Through precise trajectory planning, the robot can safely enter and exit the inlet environment, ensuring the safety and efficiency of the inspection process.
A novel robot configuration was proposed, and by integrating the genetic algorithm-particle swarm optimization (GA-PSO) algorithm and sensor fusion technology, a collision-avoidance and immersion trajectory planning method was designed. The feasibility of the proposed method was verified through computer simulation and experiments.
Through experimental verification, the air intake duct inspection robot achieved remarkable performance in the simulated air intake duct experimental environment. The robot was able to successfully complete complex inspection tasks.
The innovation of this research lies in combining machine learning with robot navigation technology and innovatively applying the GA-PSO algorithm to optimize robot trajectory planning in a complex intake environment to solve the intake inspection problem in practical applications. By autonomously optimizing the joint angles, an efficient automated inspection process is achieved, reducing the risk and cost of manual inspection. It not only optimizes the motion path of the robot, but also enhances its adaptability and stability in complex environments, providing a more efficient and reliable solution for air intake inspection.
