# Research on the Posture Control Method of Hexapod Robot for Rugged Terrain
## 1 Introduction  
The hexapod robot has abundant motion forms, redundant limb structure, good flexibility and stability, and can widely adapt to unstructured terrain. The displacement and angular displacement of the trunk coordinate system relative to the three axes, namely, X, Y, and Z, of the ground coordinate system describe the position and posture of the robot. The torso, supporting legs, and ground constitute a spatial parallel mechanism, and the rugged terrain determines the irregularity of each landing point; thus, posture control is difficult to realize [1, 2]. Posture is related to the stability, flexibility, and adaptability of a robot to complex terrain. Higher requirements are put forward for the adaptability of a robot in an unstructured environment with the progress of foot-type robot research in recent years. Therefore, posture control has become the core issue in the research of foot robots. At present, posture control methods can be divided into the following categories. In the optimal pose method [3, 4], from the perspective of trunk position control, the robot's posture is controlled to make the hexapod robot have better stability and flexibility. The stability and robustness of the torso are increased, and the effect of pose stability is achieved by designing the control algorithm [5] or designing the corresponding posture controller [6, 7] to realize the rapid adjustment of the pose. The suspension control method [8, 9]controls the posture of the robot from the perspective of force control. The hexapod robot combines the foot force perception information with the control algorithm and realizes the coincidence of foot force vector and gravity vector by the controller [10; 11] to suppress the influence of external interference on trunk posture. The force/position hybrid control method [12; 13] from the perspective of force/position hybrid control realizes stable walking by adjusting the position of the center of gravity. The three methods have the disadvantages of insufficient flexibility, heavy computational burden, and difficulty in establishing control model. In addition, scholars have tried methods such as fuzzy control [14], neural network [15], robot vision [16], data fusion [17], and virtual model creation [18] to overcome the control difficulties of the hexapod robot, such as complexity, multiple degrees of freedom, and strong coupling. The hexapod robot uses position control to realize the adjustment of the posture, which will lead to the heavy calculation burden of the controller, and ensuring the real-time performance is difficult. The force control strategy avoids the disadvantages of pure position control, but it has a poor adaptability to complex terrain and needs an accurate mathematical model to limit its application. Therefore, a pose control method which can avoid complex kinematic solution and has good operability must be designed. The hexapod robot is a multi-chain motion structure. The force distribution methods for this structure mainly include the linear programming (LP) algorithm [19], dual linear programming algorithm [20], square programming method [21], and analytical method [22]. The solution of the foot end force needs to plan the constraint conditions according to the control objective and construct the corresponding function to make a reasonable distribution of the foot force. [PERSON] et al. [23] constructed the objective function based on the quadratic convex function and realized the result of foot force distribution with continuous solution. [PERSON] [24] took the minimum internal force between feet as the optimization objective and realized the fast solution of foot force distribution in flat terrain. [PERSON] et al. [25] used friction cone constraint conditions to avoid foot slippage during walking on rough terrain and established the foot force optimization function of legs with the smallest margin.