## 1 Introduction  
Recently, the unmanned aerial vehicle (UAV) has received a wide range of urban, civilian and warfare applications [1]. It offers several extraordinary features, especially the acceptance of long-endurance and high-risk missions that could not be reasonably performed by manned aircraft. For any assignment, a common problem is that the UAVs may encounter each other. Consequently, an effective collision avoidance (CA) system is a prerequisite for free flight. There are several existing widely used or adequately evaluated CA systems for conventional aviation, and these can be applied on UAVs, such as the efficient Medium Term CA approach based on four-dimensional (4D) trajectories (trajectories defined in the three spatial dimensions together with a time-stamp) to resolve conflicts in a terminal maneuvering area (TMA) [2], the Traffic Alert and Collision Avoidance System (TCAS) equipped to issue advisories on how to maneuver vertically to prevent collisions [3], and the Airborne Separation Assurance System (ASAS) that enables the flight crew to maintain separation of an aircraft from one or more other aircraft and provides flight information concerning the surrounding traffic [4]. There are also several auxiliary systems that could provide the aircraft (or UAVs) with precise information about the state of the nearby traffic, such as the Automatic Dependent Surveillance Broadcast (ADS-B) system [5; 6; 7]. In addition, an extensive and meticulous survey of various methodologies ranging from abstract concepts to prototype systems for avoiding collisions between UAVs has been summarized in [4]. Typical techniques applied to escort UAV safety include the Monte Carlo approach [8], geometric optimization model [9], symbiotic simulation [10], probabilistic approach [11], consensus [12], evolutionary algorithm [13, 14], Kalman filter [15], Markov decision process [16], Dubins curve based algorithm [17], Predictive Control [18], reachable sets based methods [19], and so on. Each method has its own advantages and characteristics, and no matter which is employed, it aims to guarantee minimum separation between UAVs. However, some of the studies only focus on the two-UAV scenarios (not multiple vehicles). Furthermore, most of these methodologies could not support exploring the emergent dynamics between the generated trajectories and the surrounding traffic. Thus, there is a need to check the detailed states of the CA process with a global view to achieve the optimal strategy, while it is essential to understand the cause-effect relationship of each CA action and how the effects of a maneuver impact on the neighboring aircraft. Note that tight couplings between aircraft trajectories in dense scenarios (i.e., flocks applications) can deal with the propagation of non-controllable upstream and downstream maneuvers between trajectories. The overextended UAV usage of flock applications would lead to a higher number of threats, which could raise the risk of potential collisions. For simplicity, the CA problem is currently being discussed on UAVs (without pilot intervention) that are flying in a segregated airspace. In this paper, it is assumed that a group of cooperative UAVs, which are stabilized by the autopilot, are flying to their own destination and having to avoid collisions with other UAVs in an efficient real-time communication. Each vehicle is equipped with a trajectory control unit which relies on the CA algorithm that is used as a predictive control considering real-time data-link communication with surrounding traffic. In this research, we propose two efficient algorithms: conflict detection (CD) and conflict resolution (CR), which, as two sub-modules, constitute a novel CA system for multi-UAV.