Guidance, Navigation and Control

Knowledge graphs are involved in more and more applications to further improve intelligence. Owing to the inherent incompleteness of knowledge graphs resulted from data updating and missing, a number of knowledge graph completion models are proposed in succession. To obtain better performance, many methods are of high complexity, making it time-consuming for training and inference. This paper proposes a simple but effective model using only shallow neural networks, which combines enhanced feature interaction and multi-subspace information integration. In the enhanced feature interaction module, entity and relation embeddings are almost peer-to-peer interacted via multi-channel 2D convolution. In the multi-subspace information integration module, entity and relation embeddings are projected to multiple subspaces to extract multi-view information to further boost performance. Extensive experiments on widely used datasets show that the proposed model outperforms a series of strong baselines. And ablation studies demonstrate the effectiveness of each submodule in the model.

The aerial manipulator expands the scope of unmanned aerial vehicle (UAV)’s application as well as increases the difficulties in the design of the controller. To better control the aerial manipulator for different trajectories tracking under different conditions, a new dual-layer controller is designed in this paper. The integral backstepping sliding mode controller (IBSMC) is applied to the outer-loop controller and backstepping controller (BC) is applied to the inner-loop controller. To improve the performance of the system, an improved pigeon-inspired optimization (PIO) algorithm called group coevolution and immigration pigeon-inspired optimization (GCIPIO) algorithm is proposed to optimize the controller parameters of IBSMC. GCIPIO algorithm utilizes the group coevolution and immigration mechanisms. A series of simulations are conducted to show the advantage of the proposed method. The results illustrate that the proposed method ensures the closed-loop system has less end-effector tracking error.

Visual SLAM methods usually presuppose that the scene is static, so the SLAM algorithm for mobile robots in dynamic scenes often results in a significant decrease in accuracy due to the influence of dynamic objects. In this paper, feature points are divided into dynamic and static from semantic information and multi-view geometry information, and then static region feature points are added to the pose-optimization, and static scene maps are established for dynamic scenes. Finally, experiments are conducted in dynamic scenes using the KITTI dataset, and the results show that the proposed algorithm has higher accuracy in highly dynamic scenes compared to the visual SLAM baseline.

This paper presents a novel solution to the three-dimensional (3D) cooperative hunting of multiple drones that deals with surrounding a target simultaneously while navigating around obstacles in the cluttered dynamic 3D environment. Meanwhile, drones avoid the airflow downwash force created by the spinning propellers on unmanned aerial vehicles (UAVs) and their effect on the other UAVs. This solution consists of a 3D Simultaneous Encirclement strategy, the cooperative hunting objective with a novel revised particle swarm optimization (PSO*) path planning algorithm, a flocking theory-inspired obstacle avoidance algorithm, and a cascade PI controller. Simulation results with varying conditions were carried out to validate the effectiveness of the proposed solution by successfully taking care of the downwash effects, and having multiple hunter UAVs hunt and encircle a moving or stationary target in a dynamic or static obstacle-rich cluttered environment.

To identify the unsteady fluid dynamic model for a solid moving in the fluid, a new identification method that operates in the continuous time domain is proposed. It is illustrated that this new identification method is more accurate and appropriate for identifying unsteady fluid dynamic model in incompressible flow in comparison with the classical frequency domain or discrete domain method. This new method is applied to identify the unsteady fluid dynamic models of a NACA0012 airfoil and the effect of the fluid viscosity on the model parameter is analyzed. The result shows that the added mass in viscous flow is dependent on the input signal frequency and is exactly the added mass in inviscid flow when the frequency approaches infinity. Based on this discovery, the long controversy on the relationship of added mass between viscous and inviscid flow is solved. Then, the enlightenment of this discovery on the identification of both linear and nonlinear unsteady aerodynamic model in incompressible flow is illustrated. At last, the advantage of the new acquired unsteady fluid dynamic model is discussed in comparison with the classical quasi-steady aerodynamic model.

This paper models the Mars UAV formation exploring the surface of Mars, and then the formation obstacle avoidance is brought up with the assumptions of the Mars circumstance and the UAVs. Based on their specialty, constrained Delaunay triangulation, Yen-$K$ shortest path algorithm, the collaborative function, and the improved pigeon-inspired optimization (PIO) algorithm are integrated to solve the obstacle avoidance for the formation. Since the steering maneuver costs much energy and increases instabilities vulnerable in extraterrestrial exploration, the paper focuses on the route smoothness problem. The PIO is improved to be suitable for smooth routes and is compatible with other PIO variants. The simulation results show that the sum of the steering angle, namely the performance index, is effectively reduced and satisfies the obstacle avoidance requirements for Mars UAV formation.