Energy and Delay Aware Physical Collision Avoidance in Unmanned Aerial Vehicles

Abstract

Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy-and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDC-UAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collisionfree mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

Energy-aware Collision Avoidance stochastic Optimizer for a UAVs set

“…However, in real-world scenarios such assumption cannot be fulfilled due to external conditions (e.g., weather conditions) affecting the precision of sensors and GPS. Authors in [6] proposed EDC-UAV; a solution to avoid collisions within a swarm of UAVs while taking into account the margin error in GPS localization. Although EDC-UAV considers GPS's margin error, it assumes that the error is bounded following a uniform distribution.…”

“…In this way, the mission duration and the travelled distance of every UAV are minimized. The constraint (6) ensures that the probability of collision between any pair of UAVs U k and U l at the next time-step t + 1 is kept below a given probability threshold . Constraint (5) guarantees that every UAV moves forward to its targeted position at the next time-step.…”

“…This motivates the use of approaches such as Kalman-Filter to improve the accuracy of the estimated position in order to build an energy-efficient system. Figures 4,5,and 6 show the impact of the tolerated probability of collision and the number of UAVs K involved in a potential situation of collision on the extra distance, time to complete the mission, and number of collisions, respectively. As previously mentioned, these results confirms the lower the is, the more reliable will be the system (i.e., lower number of collisions) and the less energy-efficient it will become (i.e., longer traveled distance and time to complete the mission).…”

“…To address the problem of collision among a UAVs set, authors in [6] have suggested EDC-UAV solution. EDC-UAV allows to avoid collisions while UAVs life-time is extended.…”

Edge Cloud Resource-aware Flight Planning for Unmanned Aerial Vehicles

“…The optimal trajectory planning for UAV-assisted data collection in wireless sensor networks under the age of data constrains is elaborated in [16]. Work in [17], aims at optimizing the trajectory of a swarm of UAVs while avoiding the physical collisions and minimizing the mission's delay and the consumed energy. In [18], the energy-efficiency in UAV to Ground (U2G) communication is addressed via UAV's trajectory optimization.…”