MATLAB Code Implementation for Robot Formation Control

Robot formation control represents one of the core technologies in robotic cooperative operations, enabling multiple robots to move according to predefined formations. In MATLAB, we can implement animated formation control for three robots using the following approach.

First, it's essential to establish robot motion models. Common methods involve defining 2D planar position coordinates and orientation angles for each robot, utilizing single-integrator or double-integrator dynamic models to describe motion patterns. For formation maintenance, typically one robot is designated as the leader, while follower robots adjust their movements based on relative position relationships. In MATLAB implementation, this can be coded using state vectors and differential equations to simulate robot dynamics.

Second, formation control algorithms must be designed. Typical control methods include behavior-based approaches, virtual structure methods, and leader-follower strategies. In MATLAB implementation, PID controllers can be employed to regulate errors between follower robots and their desired positions. Each follower robot needs to calculate its relative position to the leader robot and generate control commands based on predefined formation parameters (such as triangular or linear formations). The algorithm can be implemented using control law functions that compute necessary velocity or acceleration inputs.

The animation implementation requires utilizing MATLAB's graphical plotting capabilities. We can create graphical windows using the figure function, then employ scatter or plot functions to update robot positions in real-time. Through loop structures and the pause command, animation effects can be generated. To enhance visualization, different colors can be assigned to different robots using color parameters, and movement trajectories can be plotted using the line or plot functions with tracking data.

To improve simulation completeness, obstacle avoidance functionality can be incorporated. When detecting that the distance between robots and obstacles falls below a safety threshold, the control strategy can be temporarily adjusted to prioritize obstacle avoidance. This can be implemented using distance calculation functions and conditional control logic. After simulation completion, motion trajectory plots and control variable change curves can be output using plotting functions for analyzing formation control stability.

This MATLAB implementation not only serves educational demonstration purposes but also provides a verification platform for practical robot formation control algorithms. By adjusting control parameters and formation configurations, researchers can study formation control performance under various conditions through parameter tuning and simulation analysis functions.