Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessit ...

/pdf/unmanned-aerial-vehicles-uavs-collision-avoidance-systems-4ng2qkrnhv.pdf

Unmanned Aerial Vehicles (UAVs) : Collision Avoidance Systems and Approaches

Mobility models serve as the foundation for evaluating and designing airborne networks (ANs). Due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the attributes of ANs. In this paper, we present a comprehensive survey and comparative analysis of mobility models that are either adapted to or developed for AN evaluation purposes. We evaluate these mobility models based on the following metrics: adaptability, networking performance, and ability to realistically capture the mobility attributes of ANs (including high mobility, mechanical and aerodynamic constraint, and safety requirements). To provide a deeper understanding and facilitate the selection and configuration of these mobility models, we also evaluate them based on randomness levels and associated applications.

A Survey and Analysis of Mobility Models for Airborne Networks

Collision avoidance strategies for multiple unmanned aerial vehicles (UAVs) based on geometry are investigated in this study. The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of UAVs. The geometric approach uses line-of-sight vectors and relative velocity vectors where dynamic constraints are included in the formation. Each UAV can determine which plane and direction are available for collision avoidance. An analysis is performed to define an envelope for collision avoidance, where angular rate limits and obstacle detection range limits are considered. Based on the collision avoidance envelope, each UAV in a formation determines whether the formation can be maintained or not while avoiding obstacles. Numerical simulations are performed to demonstrate the performance of the proposed strategies.

/pdf/collision-avoidance-strategies-for-unmanned-aerial-vehicles-3wqc7pd1yi.pdf

Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight

Unmanned Aerial Vehicles (UAVs) offer a wide range of interesting military and commercial applications at reduced costs. A couple of their more interesting characteristics include: They are capable of hovering and flying at high and low speeds, and they can move in any direction and take off and land vertically. A very promising class of these vehicles comprises small vehicles that can be flown either indoor or outdoor and offer the possibility for near-area surveillance, crop dusting, precision farming [1], microwave autonomous copter systems for geological remote sensing as shown in [2], geographic studies, fire monitoring, security applications, search- and-rescue, and attack and rendezvous [3], [4]. However, challenging requirements in terms of size, weight, and maneuverability for these systems are presented in [5]. A type of aircraft with an important and significant potential is the so-called Quadrotor that is considered by most research studies as a promising UAV [6].

A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control

Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.

A survey on unmanned aerial vehicle collision avoidance systems

This article describes the use of predictive control for the decentralized cooperative control of unmanned aerial vehicles in an unfamiliar three-dimensional environment. It is assumed that each vehicle is equipped with an autopilot and a trajectory control unit. The autopilot insures the stability of the vehicle. The setpoints of the autopilot are calculated by the trajectory control unit, thus forming a cascade control structure. The trajectory control unit relies on a predictive control algorithm to calculate the optimal commands (autopilot setpoints) such that the vehicle will reach fixed targets at known positions while avoiding static obstacles that are detected en route. The advantage in using predictive control is that it offers great flexibility in the objective function to optimize while respecting constraints such as command limits, limits on the displacement that a vehicle can carry out, and the constraints that allow obstacle avoidance. The principle proposed in order to avoid static obstacles (that are assumed ellipsoid) is to verify that the predicted vehicle trajectory does not intersect these obstacles. With the objective to increase performance, cooperation between vehicles must also be privileged. Thus, if some vehicles are within the communication range, they can share the position and shape of the obstacles they have detected. Simulations illustrate the method and higlight the benefits of cooperation.

E. Gagnon

Papers

UAV collision avoidance using cooperative predictive control