This project applies machine learning to analyze and classify flocking behavior in swarm robotics, a key concept inspired by natural phenomena like bird flocks and fish schools. By accurately identifying flocking patterns, this research supports advancements in critical fields such as search and rescue, military operations, and dynamic computer simulations.
Key Highlights:
Real-World Applications: Focused on practical use cases, including autonomous coordination in search and rescue missions, military operations, and realistic simulations for visual effects.
Comprehensive Dataset: Analyzed a dataset of over 23,000 samples with 2,400 features, capturing attributes like position, velocity, alignment, separation, and cohesion of individual agents within a swarm.
Cutting-Edge Models: Evaluated multiple machine learning classifiers, including Decision Trees, K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machines (SVM), with hyperparameter optimization using GridSearchCV and k-fold cross-validation.
State-of-the-Art Results: Achieved a testing accuracy of 90% with Decision Trees, matching the best models in the field while maintaining robust performance across metrics like F1 score and ROC-AUC.
Dimensionality Reduction: Utilized Principal Component Analysis (PCA) to reduce the feature space by 70%, improving computational efficiency without compromising model accuracy.
Insights into Behavior: Identified key features contributing to flocking behavior classification, paving the way for better swarm intelligence understanding and control.
Why It Matters:
By enhancing our ability to classify and understand collective behaviors in swarms, this project lays the foundation for more effective and intelligent autonomous systems. From coordinating rescue robots in disaster zones to creating lifelike simulations for research and entertainment, this work showcases the power of machine learning in unlocking the potential of swarm robotics.