| In recent years,with the rise of concepts such as adaptive structure and intelligent structure,structures that can actively adjust their own characteristics according to changes in the external environment have changed the design concept of traditional structures.The tensegrity structure is light,efficient,and adjustable in shape,so it has gradually attracted attention in the field of intelligent structures,and has broad application prospects in structural engineering,aerospace engineering,robotics engineering or its intersection.This paper takes the six-bar intelligent tensegrity as the research object,starts from the static balance theory and dynamic equation of tensegrity,studies its morphological control to generate the motion of the structure,and plans its motion process.Based on the point mass model,the overall morphological control equation of the intelligent tensegrity is established.In order to avoid the singularity of the mass matrix and the solution of the differential algebraic equation,the mass is concentrated at the rod end nodes to establish the dynamic equation.The initial equilibrium state of the 18-cable and 24-cable and30-cable tensegrity is obtained by dynamic relaxation;the contact force and friction force model are established,and the intelligent tensegrity motion simulation environment is established based on the built-in ODE solver of MATLAB.The generation mechanism of the basic gait of the overall motion of the tensegrity is analyzed,and the problem of finding the control strategy is transformed into a single-objective and multi-objective optimization problem,to which the particle swarm algorithm is used.Taking the offset of the center of mass as the single optimization objective,an effective driving strategy for the basic gait is obtained for the above three tensegrity,and the driving amount of each pressure rod reaches the upper and lower limits of the stroke;The Pareto solution set of each gait is obtained for the optimization objective of space,and the influence of the driving amount of different struts on the fitness of different optimization objectives is analyzed,which provides a reference for the basic gait design considering energy and internal space.The mathematical model of the path planning and trajectory planning of the 6-bar 24-cable intelligent tensegrity is established,and the configuration space of the path planning is established by the expansion method in the two-dimensional plane.The sampling-based RRT and RRT* algorithms are used to analyze the global information.The offline and online path planning is carried out on the map with known and only local information,and the effective path from the starting point to the target point is obtained.The RRT algorithm has a faster convergence speed,but the path cost is higher,and is suitable for intelligent tensegrity with limited computing resources.The path obtained by the RRT* algorithm is optimal or suboptimal,but the calculation takes a long time.For the obtained global effective path,the overall trajectory planning of intelligent tensioning is carried out.The key to trajectory planning lies in the selection of the roll axis in the rolling gait.The roll axis is determined based on the relative position of the bottom polygon and the target point.Linear trajectory planning with different travel directions and different driving strategies is carried out to verify the effectiveness of the global path trajectory planning..The physical model of the 6-bar 24-cable intelligent tensegrity was designed and produced,and the basic gait and linear trajectory planning experiments were carried out.The results show that the shape of the overall physical model of the intelligent tensioning is basically consistent with the simulation results,and the relative error of the cable length is less than 7%,which verifies the effectiveness of the basic gait driving strategy obtained by the particle swarm algorithm.There is a certain deviation between the friction model and the actual situation,and the overall travel trajectory of the tensegrity has a certain deviation from the simulation,but the movement from the starting point to the target point is completed,which verifies the effectiveness of the trajectory planning strategy to a certain extent. |
