Context. Simulation of nature is something that is used to immerse the player into the world of games. By adding details in the world such as birds circling in the sky or small fishes swimming in a flock, developers can improve the gaming experience for the user. More precise simulations are something that should be aspired for. This thesis will explore the boid flocking algorithm and evaluate what settings users perceive as realistic behavior for simulating schools of fish. Objectives. This thesis proposes that there should be a set of variables that reflect a more realistic behavior and through gathering data from volunteers and mapping their answers, conclude if that statement is true. Methods. A boid simulation will be run in a number of different scenarios, each differing in variables that are vCohesion, vSeparationand vAmount that make changes to the overall behavior. This behavior is then recorded and compared next to each other in a perceptual experiment with the objective of finding out the preferred settings interms of realism. Results. The experiment showed that the preferred value of vSeperation was around 50 to 60 world units. The value of vCohesion and vAmount was random to what was perceived, so their impact on realism was not significant enough. Conclusions. After running the experiment it was apparent that there was a preferred value on some of the variables that were examined. The larger impact on realism was in the distance each boid wanted to keep from its neighbor, the vision range of each boid defined what was considered a neighborhood. The range on this variable was not of much importance and did not impact what the user perceived as realistic.

Several of our technological breakthroughs are influenced by types of behavior and structures developed in the natural world, including the emulation of swarm in- telligence and the engineering of artificial synapses that function like the human mind. Much like these breakthroughs, this report examines emerging behaviors across swarms of non-communicating, adaptive units that evade obstacles while find- ing a path, to present a swarming algorithm premised on a class of local rule sets re- sulting in a Unmanned Aerial Vehicle (UAV) group navigating together as a unified swarm. Primarily, this method’s important quality is that its rules are local in nature. Thus, the exponential calculations which can be supposed with growing number of drones, their states, and potential tasks are remedied. To this extent, the study tests the algorithmic rules in experiments to replicate the desired behavior in a bounded virtual space filled with simulated units. Simultaneously, in the adaptation of natural flocking rules the study also introduces the rule sets for goal seeking and uncertainty evasion. In effect, the study succeeds in reaching and displaying the desired goals even as the units avoid unknown before flight obstacles and inter-unit collisions with- out the need for a global centralized command nor a leader based hierarchical system.

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