# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 5 Simulation Analysis of Obstacle Avoidance Steering Control
### Differential Steering Simulation Analysis  
In the process of differential steering, the robot needs to master the size, direction, and road parameters of obstacles in advance to determine the track driving parameters. Because the robot is driving under different road conditions, although there are the same driving parameters, the steering effect of the robot will be different. When the robot travels on different road surfaces, even with the same driving parameters, the robot's steering performance varies. For instance, on rough terrain, the robot can achieve obstacle avoidance steering with a smaller turning radius. However, when using the same driving parameters to steer the robot on smoother terrain, the robot requires a larger turning radius, which may lead to collisions with nearby obstacles.  
This study defines two types of road surface conditions: one is a rough road surface, and the other is a smooth road surface, each characterized by a different S_coefficient and D_coefficient. Scheme 1 involves the tracked robot using control strategy 1 for obstacle-avoidance on a rough road surface. Scheme 2 involves the tracked robot using control strategy 1 for obstacle avoidance on a smooth road surface. Scheme 3 involves the tracked robot using control strategy 2 for obstacle avoidance on a smooth road surface, as depicted in Figure 13.  
The robot adopts the same control strategy for obstacle-avoiding steering movements on different roads, but their trajectories are completely different, as shown in Figure 13a,b. By adjusting the driving parameters of the robot track, the obstacle avoidance steering movement is completed according to the requirements, as shown in Figure 13c.  
Table 3 lists the simulation parameters and results for the robot in the ADAMS virtual prototype. where Ra represents the actual steering radius, Ra' represents the theoretical steering radius, and the numbers 1 and 2 represent the number of steering control strategies. It can be observed that the actual steering radius of the robot is larger than the theoretical steering radius, particularly when the robot is driving on poor roads, and the difference between the theoretical and actual values is larger.  
The theoretical and actual velocity curves of the left and right tracks during the steering process of the robot are shown in Figure 14, where R and L represent the right and left tracks, respectively, and V and V\({}^{\prime}\) represent the actual traction speed and theoretical traction speed, respectively. The numbers 1, 2, and 3 represent the serial numbers of the three control schemes. Figure 14a,b show the obstacle avoidance steering motions of the robot using the same control strategy on different roads; however, their velocities are completely different. By adjusting the driving parameters of the robot track, the robot completed the obstacle avoidance steering motion, as required, as shown in Figure 14c,d. Through the simulation analysis of differential steering, it becomes evident that the speed of the robot's two tracks undergoes minimal change, enabling the robot to adep  
Figure 13: Obstacle avoidance steering trajectory of the robot using large-radius steering: (**a**) Scheme 1; (**b**) Scheme 2; (**c**) Scheme 3.  
turned to the right, the left track skidded and its value was positive. The right track slipped, and its values were negative, as shown at point C. Based on the comparative analysis of Figure 15a,b, it is evident that when the robot navigates on a bad road surface, the skid and slip rates of the robot are greater compared to when the robot operates on a good road surface. Through the adjustment of the driving parameters of the two tracks, the robot can effectively execute obstacle avoidance steering maneuvers, leading to a reduction in both the sliding rate and slip rate of the robot, as shown in Figure 15c. The skid and slip rate changes of the robot under different road and control strategies are shown in Table 4.