Multi-Agent Systems, often referred to a network of loosely connected autonomous units, are widely used to model the dynamics of crowds, vehicles, robots and swarms in traffic management, biological environment, distributed control and communication technologies. Recently, the study of multi-agent systems is rapidly growing due to the beneficial advantages of using a team of agents in logistics, mapping, search and rescue, etc.

In this thesis, we focus on the problem of decentralized collision avoidance among multiple intelligent moving agents. Imaging each agent to be a human, a moving car, or an aircraft, it is supposed to make its decision independently based on its perception of the local environment through sensors only. Two different collision avoidance protocols are presented to generate updated reference velocity continuously for each agent that leads to no future collision. The first method, the rotation based method, is adopted by a geometric based algorithm introduced for 2-Dimensional space and the second method, the potential fields based method, could be categorized as an example of Harmonic Potential Fields (HPF) discussed by Masoud. Both methods employing position information of obstacles result in collision-free paths for each agent under the assumption that all other agents following similar maneuvers. Through simulations in MATLAB, satisfied performances are achieved for method 2 in all challenging scenarios we set while method 1 faced some difficulties dealing with multi-obstacle simultaneously as well as 3D scenarios.