Biological inspiration (ants, bees, birds, fish schooling), Boids model (separation, alignment, cohesion), Reynolds rules and flocking behavior, Particle Swarm Optimization (PSO: velocity update, inertia weight), Ant...
Biological inspiration (ants, bees, birds, fish schooling), Boids model (separation, alignment, cohesion), Reynolds rules and flocking behavior, Particle Swarm Optimization (PSO: velocity update, inertia weight), Ant Colony Optimization (ACO: pheromone trails, evaporation), Artificial Bee Colony (ABC), Swarm stability analysis.
Communication topologies (centralized, decentralized, hybrid), Consensus algorithms (average, leader election), Task allocation (CBBA, auction-based), Formation control (virtual structures, leader-follower), Potential field methods (artificial potentials, collision avoidance), Graph-theoretic approaches (Riemannian manifolds).
Coverage control (Voronoi partitions, Lloyd algorithm), Foraging and patrolling strategies, Flocking with obstacles, Self-assembly and shape formation, Density control and deployment, Scalable coordination (local interactions, emergent behavior), Scalability analysis (communication overhead, convergence time).
Multi-robot SLAM (centralized, decentralized, DDF-SLAM), Data association (JPDDA, Murty's algorithm), Cooperative localization (EKF/UKF fusion), Relative pose estimation (anchor nodes), Graph SLAM optimization (pose graph, factor graphs), Outlier rejection and robust estimation.
Heterogeneity in swarms (specialized roles), Fault tolerance and resilience, Learning in multi-agent systems (MARL, QMIX), Swarm reinforcement learning, Real-world applications (search-rescue, agriculture, warehouse automation), Hardware platforms (Khepera, Kilobot, TurtleBot swarm), Standardization (ROS2 multi-robot, MQTT/IoT protocols).