Peng Yao

Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle

This paper proposes a novel Dynamic Adaptive Ant Lion Optimizer (DAALO) for route planning of unmanned aerial vehicle. Ant Lion Optimizer (ALO) is a new intelligent algorithm motivated by the phenomenon that antlions hunt ants in nature, showing the great potential to solve the optimization problems of engineering. In the proposed DAALO, the random walk of ants is replaced by Levy flight to make ALO escape from local optima more easily. Besides, by introducing the improvement rate of population as the feedback, the size of trap is adjusted dynamically based on the 1/5 Principle to improve the performance of ALO including convergence accuracy, convergence speed and stability. Compared to some other bio-inspired methods, the proposed algorithm are utilized to find the optimal route in two different environments such as mountain model and city model. The comparison results demonstrate the effectiveness, robustness and feasibility of DAALO.

A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints

Nonlinear optimal control (NOC) problem with complex dynamic constraints (CDC) is difficult to compute even with direct method. In this paper, a hybrid two-stage approach integrating an improved backtracking search optimization algorithm (IBSA) with the hp-adaptive Gauss pseudo-spectral methods (hpGPM) is proposed. Firstly, BSA is improved to enhance its convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback. Then, at the beginning stage of the hybrid search process, an initialization generator is constructed using IBSA to find a near optimum solution. When the change in fitness function approaches to a predefined value which is small enough, the search process is replaced by hpGPM to accelerate the search process and find an accurate solution. By this way, the hybrid algorithm is able to find a global optimum more quickly and accurately. Two NOC problems with CDC are examined using the proposed algorithm, and the corresponding Monte Carlo simulations are conducted. The comparison results show the hybrid algorithm achieves better performance in convergent speed, accuracy and robustness. The improved BSA (IBSA) is proposed to enhance the convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback.The hp-adaptive gauss pseudospectral method (hpGPM) is adopted to overcome the drawbacks of the fixed discrete points generated during the conventional controls parameterization.A novel hybrid two-stage optimization algorithm integrating the IBSA with the hpGPM is proposed to find a global optimum more quickly and accurately.

Path planning for solar-powered UAV in urban environment

Abstract Aiming at the complexity and particularity of urban environment, a solar-powered UAV (SUAV) path planning framework is proposed in this paper. The framework can be decomposed into three aspects to resolve. First, to make SUAV avoid the building obstacles, a nature-inspired path planning method called Interfered Fluid Dynamical System (IFDS) is introduced. Aiming at the defect that the traditional IFDS is not suitable for SUAV energy optimization calculation, the dynamic constraints and model are introduced to IFDS. The modified IFDS, called Restrained IFDS (RIFDS), is proposed. Second, to resolve the path planning issue efficiently, a novel intelligent optimization algorithm called Whale Optimization Algorithm (WOA) is selected as the basic framework solver. To further overcome the drawback of local minima, adaptive chaos-Gaussian switching solving strategy and coordinated decision-making mechanism are introduced to the basic WOA. The modified algorithm, called Improved WOA (IWOA), is proposed. Third, to solve the accurate modeling problem of solar energy in urban environment, two measures are adopted: (1) A practical judgment method for sunlight occlusions is proposed; (2) Aiming at some unreasonable aspects in the solar energy production model, the received solar energy is modified and recalculated by ASHRAE Clear Sky Model and the solar irradiance calculation principle for slant surfaces in this paper. Finally, the effectiveness of the proposed framework is tested by the simulations.

3-D dynamic path planning for UAV based on interfered fluid flow

A 3-D dynamic path-planning algorithm based on interfered fluid flow (DA) is proposed for unmanned aerial vehicle (UAV) in dynamic environment. This paper first describes the mathematical modeling of convex obstacles. Then the static algorithm based on interfered fluid flow (SA) is introduced and improved, whose planning path conforms to the general characteristic of phenomenon that running water can avoid rock and arrive at destination. By updating the information of obstacles and obtaining the relative velocity, we then transform the dynamic problem to static problem. Therefore, DA is simplified by adopting SA. Finally, this dynamic algorithm proves to be efficient for real-time path planning by simulation results.

Autonomous aerial refueling precise docking based on active disturbance rejection control

In this paper, the receiver aircraft of the AAR is modeled, and the disturbances are presented. The longitudinal and the lateral model of the receiver are correspondingly transformed into relevant three second-order systems and a third-order system. It makes the motion model more convenient for controller design, and the scale separation is avoided at the mean time. Then, the ADRC is firstly applied to the precise docking controller design to adequately reject the different complex disturbances during the docking of AAR. Comparative simulation results show that the ADRC achieves better performance in AAR docking control under complex flow interference.

Hybrid UAV path planning based on interfered fluid dynamical system and improved RRT

In this paper, a hybrid strategy based on interfered fluid dynamical system (IFDS) and improved rapidly-exploring random tree (IRRT) is proposed for the unmanned aerial vehicle (UAV) route planning problem in 3-dimensional complex environments. By imitating the phenomenon of fluid flow, the IFDS method can plan a smooth and safe path quickly, but the route may fall into the concave area produced by some overlapping obstacles. Hence the IFDS method is combined with IRRT, which introduces the target probability and heuristic evaluation function on the basis of the traditional RRT. In this hybrid method, the IRRT method can be adopted as the framework of route planning, where the expanding nodes can be computed by IFDS algorithm. The simulation results by different methods prove that this method is of good performance of space searching and obstacle avoidance in 3-dimensional path planning.

UAV collision avoidance decision and 3D path re-planning method based on improved interfered fluid dynamical system and rolling optimization

In this paper, we propose a hybrid 3D collision avoidance approach based on improved interfered fluid dynamical system (IFDS) and rolling optimization. First, a collision avoidance decision making mechanism based on our previously proposed dynamic collision regions is introduced. Then a path re-planning method based on improved IFDS and rolling optimization is proposed. Rolling optimization is used to optimal the influence coefficients of IFDS with the UAV performance constraint. Finally a UAV three-dimensional re-planning path can be obtained. Simulation results indicate that the proposed method could choose appropriate collision avoidance time and maneuver flight mode, and plans a smooth collision avoidance path in the case of single and multiple intruders.

On terrain-aided navigation for unmanned aerial vehicle using B-spline neural network and extended Kalman filter

Terrain-aided navigation technology estimates position information based on the terrain elevation data, and corrects the inertial navigation system (INS) error. A terrain matching algorithm based on B-spline neural network and extended Kalman filter (EKF) is proposed for unmanned aerial vehicle (UAV). In order to improve the accuracy of traditional terrain linearization method, B-spline neural network is applied to fit terrain data. Terrain-aided navigation (TAN) system often need to preload digital elevation map (DEM), so offline training of the neural network using the actual terrain data is practical. The neural network calculates the continuous terrain elevation and terrain gradient. Then these data are used in EKF. The simulation results show that the B-spline neural network can calculate the high-accuracy linearized terrain data on the DEM, and the performance of TAN system is better by using EKF combined B-spline neural network method.