Multi-Robot Programming Design with Fuzzy Logic Algorithms

In this article, we will discuss MATLAB-based multi-robot programming design and introduce the implementation of fuzzy logic algorithms. This validated approach has demonstrated excellent performance in practical applications. The program employs fuzzy logic controllers to handle uncertainty and imprecision in robotic systems, typically implemented through MATLAB's Fuzzy Logic Toolbox with membership functions and rule bases defined using fis (Fuzzy Inference System) files. The algorithm enhances robots' adaptability to environmental changes by processing sensor data through fuzzification, inference engine operations, and defuzzification stages. Key functions like addvar(), addmf(), and addrule() are used to construct the fuzzy logic system, while evalfis() executes the inference process. This implementation improves movement efficiency and accuracy, leading to superior performance across various scenarios. Furthermore, we will explore parameter tuning methodologies for different application scenarios. This includes adjusting membership function parameters using setfis() and optimizing rule weights through genetic algorithms or particle swarm optimization to achieve optimal performance. We hope this technical information proves valuable for your multi-robot system development!