An Obstacle Avoidance Scheme Maintaining Connectivity for Micro-Unmanned Aerial Vehicles

Abstract

This paper suggests an obstacle avoidance scheme that enables a group of micro-UAV (Unmanned Aerial Vehicles) to avoid colliding with an obstacle that is found in the course of the flight. This scheme considers a method for UAVs to avoid colliding with such obstacles and a plan of action for when the UAVs in the group lose connectivity during flight. The main goal is for UAVs to reach the target without colliding with any obstacles. To achieve this goal, directly after the UAVs avoid the obstacle at a raised altitude above it, they fly at a higher altitude for a while and then descend to their original height. When this approach is judged to be inefficient because the height of the obstacle is too high, the UAVs divide into two groups and move to either side of it while continuing to fly at the same altitude, thereby avoiding the obstacle. Afterwards, they gather into one group again. We verify the proposed scheme and do the performance evaluation by ns-2.

Energy Efficient Data Dissemination in Multi-UAV Coordinated Wireless Sensor Networks

“…where is the average radio range of the sensor node. From (11) and (12), the attraction for each node in UAVs to sensors and UAV to UAV mapping is computed as…”

“…set Flag = 0 (3) while (transmission != end) do (4) markthesource(∈ ) (5) broadcast "Hello" ← (6) = segment of current source (∈ ) (7) while (next hop found != true) do (8) sort nodes in order of their attraction (9) select upper node and check for its connections (10) if (connections available == true) then (11) c o n t i n u e (12) else (13) select next optimal node (14) end if (15) check for satisfaction of (9) and (15) (16) if (satisifaction == true) then (17) select node as next hop (18) F l a g=0 (19) else (20) F l a g=1 (21) end if (22) UAVs coordination, and UAV to UAV coordination, as shown in Algorithms 1, 2, and 3, respectively. Algorithm 1 is used in case of sensors to sensors routing.…”

“…With the network node selected on the principle of the attraction property of the fire fly, the value of attraction changes after each iteration, thus eradicating the chance of selection of similar node that may cause the loop in the network. A routing spanning tree comprising the nodes with maximum attraction is formed which primarily selects the nodes from the different segments, thus advancing while ( ≤ ) do (6) check for nearest UAVs following rules in (9) and 15) (7if (UAVs are Identified) then (8) count = number of such UAVs (9) if (count == 1) then (10) S e l e c tt h eU A Vw i t h = max (11) else (12) Select the UAV more close to the Base Station (13) end if (14) end if (15) = + 1 (16) end while (17) end while Algorithm 2: Data dissemination: sensors to UAVs.…”

“…(1) Input: , (2) = number of UAVs per segment, such that = × (3) while (transmission != end) do (4) if ( == 1) then (5) = select the UAV (6) fi n dl oc a ti o nf o r th UAV 7if (base station is within the range of th UAV) then (8) transmit() (9) exit(0) (10) else (11) broadcast message for new attraction value (12) sort and select the UAV with highest attraction value (13) repeat check from step (5) onwards (14) end if (15) else (16) mark the distance and attraction vale (17) if (count(distance and attraction vale) > 1) then (18) select the node which has not be used earlier (19) else (20) transmit() (21) exit(0) (22) end if (23) end if (24) end while Algorithm 3: Data dissemination: UAV to UAV. data towards the base station.…”

“…The collaboration between the UAVs and the WSNs can provide a vast range of applications such as sensor flocking, border surveillance, area monitoring, remote data gathering, and obstacle avoidance [6,[9][10][11]. Collaborative network between the UAVs and the WSNs can provide an efficient solution to the routing loop problem in traditional sensor networks and can also enhance the lifetime of the sensors 2 Mobile Information Systems by optimal division of the load.…”