In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot insp...In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm’s movement.The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion.To enhance the robot’s movement efficiency and reduce wear on the adsorption device,a gait mimicking an inchworm’s movement is planned,and foot trajectory planning is performed using a quintic polynomial function.Under velocity constraints,foot trajectory optimization is achieved using an improved Particle Swarm Optimization(PSO)algorithm,determining the quintic polynomial function with the best fitness through simulation.Finally,through comparative experiments,the climbing time of the robot closely matches the simulation results,validating the trajectory planning method’s accuracy.展开更多
基金supported by the Guangxi Science and Technology Base and Talent Project(AD23026115)the Special fund for centrally guided local science and technology development(2023JRZ0103)+1 种基金the Guangxi University of Science and Technology Doctoral Fund(2023KY0353)the Guangxi University of Science and Technology Doctoral Fund(22Z39).
文摘In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm’s movement.The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion.To enhance the robot’s movement efficiency and reduce wear on the adsorption device,a gait mimicking an inchworm’s movement is planned,and foot trajectory planning is performed using a quintic polynomial function.Under velocity constraints,foot trajectory optimization is achieved using an improved Particle Swarm Optimization(PSO)algorithm,determining the quintic polynomial function with the best fitness through simulation.Finally,through comparative experiments,the climbing time of the robot closely matches the simulation results,validating the trajectory planning method’s accuracy.
