In a three-dimensional (3D) disaster rescue mission environment, multi-drone mission assignments and path planning are challenging. Aiming at this problem, a mission assignment method based on adaptive genetic algorithms (AGA) and a path planning method using sine–cosine particle swarm optimization (SCPSO) are proposed. First, an original 3D digital terrain model is constructed. Second, common threat sources in disaster rescue environments are modeled, including mountains, transmission towers, and severe weather. Third, a cost–revenue function that considers factors such as drone performance, demand for mission points, elevation cost, and threat sources, is formulated to assign missions to multiple drones. Fourth, an AGA is employed to realize the multi-drone mission assignment. To enhance convergence speed and optimize performance in finding the optimal solution, an AGA using both the roulette method and the elite retention method is proposed. Additionally, the parameters of the AGA are adjusted according to the changes in the fitness function. Furthermore, the improved circle algorithm is also used to preprocess the mission sequence for AGA. Finally, based on the sine–cosine function model, a SCPSO is proposed for planning the optimal flight path between adjacent task points. In addition, the inertia and acceleration coefficients of linear weights are designed for SCPSO so as to enhance its performance to escape the local minimum, explore the search space more thoroughly, and achieve the purpose of global optimization. A multitude of simulation experiments have demonstrated the validity of our method.

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.

In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed for low-altitude unmanned aerial vehicles (UAVs) multiple-input-multiple-output (MIMO) air-to-ground (A2G) channel. In the proposed model, both UAV rotation and arbitrary trajectories are taken into account. UAV rotation is described by a 2D Gauss-Markov random mobility model (RMM), while UAV arbitrary trajectories are characterized by a 3D revised Gauss-Markov RMM. The time-varying parameters of multipath components (MPCs), such as distances, angles of arrival (AoAs), and angles of departure (AoDs), are calculated based on the proposed model. In addition, the expressions of the space-time correlation function (CF) and Doppler power spectral density (PSD) are also derived. Numerous numerical simulations are conducted and the simulation results reveal that the UAV rotational motion causes channel non-stationarity in space domain, and the mobility characteristics of UAV along with arbitrary trajectories cause channel time domain non-stationary. Finally, the accuracy of the model is verified by comparing with the available measurement data.

Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object’s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.