Observations in biologically inspired swarm formations from nature, like flocks of birds, herds of mammals, and packs of wolves, have inspired the innovation of various multi-robotic architectures. This work presents a robotic system that mimics leader-follower behaviors in the navigation and formation of sparse and dense environments. This work extends the original work by Weitzenfeld et al. to evaluate new swarm-based multi-robot architectures with obstacle avoidance and variations in group formations. The multiple robot architecture is based on a wolf pack with a defined ‘alpha wolf,’ which acts as the leader, and defines‘betas wolves,’ which act as followers. The ‘alpha wolf’ leads multiple ‘beta wolves’ that follow in formation behind the lead wolf, keeping track of a group member and maintaining a set angle and distance while performing obstacle avoidance, staying in formation, and performing speed adjustment. Variations in swarm formation behaviors being analyzed with robots include (1) beta robots following the alpha robot, (2) beta robots following the closest neighboring robot, and (3) robots following the same robot identified since the beginning. Experiments are performed in simulation, using Webots, to analyze robot formations.