This study proposes a new flight route-planning technique for autonomous navigation of unmanned aerial vehicles (UAVs) based on the combination of evolutionary algorithms with virtual potential fields. By combining a radial force field with a swirling force field, three-dimensional virtual potential fields are constructed for repelling infeasible UAV flight routes from threatening zones. To ensure feasibility, major flight constraints are considered when searching for the optimal flight route. This study examines both single-and multiple-obstacle cases to determine the efficiency of the proposed flight route planner. The UAV navigation method uses an offline planner in known environments and an online planner for flight route replanning when popup threats emerge. Both planners were tested under various scenarios. The results show that the proposed planner can efficiently enable the safe navigation of UAVs.
NomenclatureCroute k = objective term in a neural network system d k;i;j = distance of ith waypoint of kth flight route to jth obstacle froute k = constrained objective function in a neural network system F max = parameter used to modulate the potential field within R j and S j F Total k;i = resultant force influencing ith node of kth flight route F Multi k;i;j = virtual potential force F xy , F z = horizontal and virtual potential forces L max , L min = maximal and minimal lengths of route segment and amplitude of oscillation O xyz = virtual potential field O w i = swirling repulsive force o Multi j = coordinates of overall center mass Proute k = penalty term in a neural network system p n = end node; goal position p 0 = starting position p 1 = second node q f k;i = correction for ith node in kth chromosome according to magnitudes of virtual force R j = minimal radius measured from o j , which covers entire obstacle S j = sphere of influence (radial extent of force from o j ) s k;i = vector of ith segment in cylinder diameter of kth chromosome T 0 xyz = virtual force field of threat areas near dangerous zone v xy , v z = horizontal and vertical velocities x dir g , y dir g , z dir g = expected direction at goal position x dir s , y dir s , z dir s = direction of unmanned aerial vehicle at starting position max = maximal climbing angle of unmanned aerial vehicle max = maximal diving angles of unmanned aerial vehicle max = maximal turning angle of unmanned aerial vehicle x , y , z = direction of magnitude of mutation on corresponding axis