This study addresses the flight-path planning problem for multirotor aerial vehicles (AVs). We consider the specific features and requirements of real-time flight-path planning and develop a rapidly-exploring random tree (RRT) algorithm to determine a preliminary flight path in three-dimensional space. Since the path obtained by the RRT may not be optimal due to the existence of redundant waypoints. To reduce the cost of energy during AV's flight, the excessive waypoints need to be refined. We revise the A-star algorithm by adopting the heading of the AV as the key indices while calculating the cost. Bezier curves are finally proposed to smooth the flight path, making it applicable for real-world flight.

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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

This paper develops an energy recharging controller (ERC) for electrical scooters (ESs) which includes a recharging control unit, a battery recharging control unit, a voltage-boost control unit, a braking control unit, a motor and a battery. There are three recharging modes in the proposed ERC: low-side driving circuit recharging mode, boost recharging mode, and brake assistant mode. With the proposed energy recharging control mechanism, it is shown that mechanical energy can be efficiently converted into electrical energy for energy conservation and the controller would extend the ES's touring range. To ensure safety of the recharging system during battery recharge, a protecting circuit is developed.

This paper presents a new approach with a fuzzified eigensystem realization algorithm for identification of flight vehicle models in low-speed wind tunnel (LSWT) and high-speed wind tunnel (HSWT). A variety of variables in model types and testing environment (such as angle-of-attack, sideslip angle, tunnel wind speed) and profile, elevator, and power system (motor and propeller) of mini unmanned aerial vehicle (mini-UAV) model are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify the effectiveness of the proposed methodology, simulations are conducted using real-world experimental results that demonstrate that the working performance of the proposed method correlates well as expected