The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area A highly efficient route planning approach is required for this complex high dimensional optimization problem However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm Finally, the Cauchy-Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration

https://www.mdpi.com/1424-8220/21/4/1224/pdf

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV.

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

/pdf/uav-formation-trajectory-planning-algorithms-a-review-2lxo60sj.pdf

UAV Formation Trajectory Planning Algorithms: A Review

An improved simulated annealing-artificial potential field (SA-APF) algorithm is proposed to solve the problem of large-scale dense unmanned aerial vehicle (UAV) formation path planning in three-dimensional space. First, the artificial potential field (APF) method is extended to three dimensions and combined with the kinematics of the drone to constrain the step length, a path that meets the kinematics of the drone is generated and all UAVs can reach the target synchronously. By designing a double-layer repulsive potential field, establishing a collision prediction and obstacle avoidance mechanism, the defects of the shock path of the traditional APF are overcome. In order to reduce the computational complexity of dense formation path planning, the simulated annealing (SA) algorithm is used to optimize the distance cost, and the optimal allocation is calculated to replace the initial task allocation for path planning. Finally, the improved SA-APF algorithm is used for simulations and flight experiments on 1,000 UAVs. The results show that the path generated by SA-APF is smoother and has lower complexity. The algorithm can greatly reduce resource consumption in actual application.

Path Planning for Large-scale UAV Formation Based on Improved SA-APF Algorithm

To prevent the locally optimal problem and slow convergence problem of unmanned vehicles (UVs) path planning, an improved ant colony algorithm is proposed by using a dynamic pheromone volatility coefficient. The best path is searched by selecting the appropriate pheromone volatility coefficient in ant colony algorithm, which has better searching ability, and converges to the optimal value quickly. The experimental results are illustrated to compare with other improved ant colony optimization algorithms to verify the effectiveness and efficiency of our proposed path planning method for UVs.

Optimal Path Planning for Unmanned Vehicles Using Improved Ant Colony Optimization Algorithm

Multiple unmanned aerial vehicles (UAVs) formation has a broad application prospects, which will be used widely in the civilian and military fields. An improved ant colony algorithm method is proposed to solve the problem of path planning in formation transformation. Firstly, we designed a location assignment based on ant colony optimization algorithm. Then a new node selection strategy is proposed to improve the optimization results. At the end, the simulation results show the effectiveness and progress of the method. The improved method proposed in this paper can avoid the results into the local optimization and the algorithm optimization speed has increased.

Path Planning for Multi-Rotors UAVs Formation Based on Ant Colony Algorithm

In this paper, taking quadrotor as the research object, a kind of airborne vision system is presented to detect and track the moving target. Lower cost and better performance are the remarkable characteristics of the system. Firstly, the overall scheme of the detection and tracking system is designed. Secondly, a new image detection algorithm based on HSV color model and template matching is proposed, then according to image principle, the relative distance from quadrotor to target is calculated. In order to solve the time delay caused by image processing, the target position prediction is made by adding Kalman filter. Then, according to the position information of quadrotor and target, a segmented PID controller is designed to improve the tracking effect. Finally, the algorithm is applied to the actual flight of the quadrotor to verify its feasibility.

Realization of the Airborne System for Moving Target Detection and Tracking Based on Quadrotor

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Path Planning for Multi-Rotors UAVs Formation Based on Ant Colony Algorithm

Realization of the Airborne System for Moving Target Detection and Tracking Based on Quadrotor