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Unmanned aerial vehicles (UAVs) have attracted considerable research attention because of their strong interoperability, high flexibility, and excellent maneuverability. Path planning and autonomous obstacle avoidance are critical for UAVs. In this study, multiobjective optimization using the ant colony algorithm was performed for solving the UAV obstacle avoidance path planning problem. To overcome the easy-to-fall-into-deadlock tendency and slow convergence speed of the conventional ant colony algorithm, an elite ant colony algorithm was proposed for improving path selection probability and pheromone update strategy. Next, the response surface method was used to analyze the key parameters in the improved algorithm, construct the regression prediction model of response indicators, perform variance analysis, and verify the reliability of the model. The key parameters were optimized to obtain the best parameter combination, and simulation experiments were conducted. The results revealed that the performance of path length, running time, and robustness in various terrains improved considerably. Thus the proposed method is a feasible scheme for the path planning of UAVs in military search and rescue and material transportation. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) path planning is an essential branch in UAVs research. This paper presents the hardware implementation of the UAV path planning problem using an improved parallel genetic algorithm (GA) in a multi-microcontroller environment. A controller area network (CAN) bus is a robust bus designed to allow microcontrollers to communicate with each other in applications without a host computer. The CAN bus is used to communicate between the microcontrollers and solve the path planning problem with the parallel algorithm. The data exchange on this network is by the multi-master model, so it is possible to implement an asynchronous and multi-master parallel algorithm using CAN bus. Also, we use the 32-bit ARM Cortex-M3 microcontroller (with CPU clock up to 100 MHz) for hardware implementation. The comparison of both single and parallel GA shows that a multi-microcontroller structure produces better results on the CAN bus, and the parallel version experiences significantly faster speeds than the sequential version. [ABSTRACT FROM AUTHOR]

A multistatic unmanned aerial vehicle-borne synthetic aperture radar (MuUAV-SAR) plays an important role in the applications of environmental monitoring and disaster warning because its distributed platforms can provide high-resolution imagery by fusing the multiple measurements. However, the flight paths of the multiple platforms are limited for such an unmanned system since the flight safety and the path length are basic conditions for guaranteeing the effective observation. This article first studies the observation signal model of the MuUAV-SAR imaging system and then analyzes the factors that determine the imaging resolution, while these factors are all determined by the flight path of unmanned aerial vehicles (UAVs). Second, an MuUAV-SAR imaging path planning problem is established as a constrained multiobjective optimization problem (CMOP), which considers the navigation and imaging performance of the UAV in the process of completing the path planning task in detail. For this CMOP, a heuristic search method is proposed to solve it, which can ensure that each step achieves local optimum, and it can also list all feasible solutions to meet the application requirements for selection. Finally, experimental results verify the effectiveness and practicability of the proposed heuristic path planning method.

The unmanned aerial vehicle (UAV) has been a research focus in recent years. The path planner is a key element of the unmanned aerial vehicle autonomous control module. In this paper, the modified wolf pack search (WPS) algorithm is applied to compute the quasi-optimal trajectories for the rotor wing UAVs in the complex three-dimensional (3D) spaces including the real and fake 3D spaces. Moreover, it adopts the multi-objective cost function. In the path planning process, some concepts in the genetic algorithm (GA) are applied to realize the WPS algorithm. Then, the crossover and mutation operators in the GA method are introduced to improve the original WPS algorithm. Considering the dynamic properties of the vehicle, the path smoothing process based on the cubic B-spline curve is used to make the planning path suitable for the fixed wing UAVs. Simulation results show that this approach is efficient for the rotor wing UAVs and the fixed wing UAVs when taking into account of all kinds of constraints and the path generated is flyable. Moreover, the comparisons of the four algorithms show that the trajectories produced by the modified WPS algorithm are far superior to the original WPS algorithm, the GA and the random search way under the same conditions. [ABSTRACT FROM AUTHOR]

Path planning for the three-dimensional (3D) unmanned aerial vehicles (UAV) is a very important element of the whole UAV autonomous control system. In this paper, a modified central force optimization (MCFO) method is introduced to solve this complicated path-optimization problem for the rotary wing vertical take-off and landing (VTOL) aircraft. In the path planning process, the idea from the particle swarm optimization (PSO) algorithm and the mutation operator of the genetic algorithm (GA) are applied to improve the original CFO method. Furthermore, the convergence analysis of the whole MCFO method is established by the linear difference equation method. Then, in order to verify the effectiveness and practicality of this new path planning method, the path following process is put forward based on the six-degree-of-freedom quadrotor helicopter control system. At last, the comparison simulations among the six algorithms show that the trajectories produced by the whole MCFO method are more superior than the original CFO algorithm, the GA, the Firefly algorithm (FA), the PSO algorithm, the random search (RS) way and the other MCFO algorithm under the same conditions. What is more, the path following process results show that the path planning results are practical for the real dynamic model of the quadrotor helicopter. [ABSTRACT FROM AUTHOR]