To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities through the particle swarm optimization (PSO) algorithm. The optimal consensus control algorithm is then employed to achieve the optimal convergence rate of the UAV formation. To mitigate the limitations of traditional APF, a collinear force deflection angle is introduced, along with an obstacle avoidance method aimed at preventing UAVs from being trapped in locally optimal solutions. Additionally, an obstacle avoidance algorithm based on virtual force fields between UAVs is designed. Comparative analysis against the basic algorithm demonstrates the effectiveness of the designed optimal consensus algorithm in improving formation convergence performance. Moreover, the improved APF resolves local optimal solution issues, enabling UAVs to effectively navigate around obstacles. Simulation results validate the efficacy of this method in achieving multi-UAV formation control while effectively avoiding obstacles.

To address the issues of local minima, target unreachability, and significant formation disruption during obstacle avoidance in the conventional artificial potential field (APF), a control approach that integrates APF with optimal consensus control which can achieve cooperative obstacle avoidance is proposed. Based on the double integrator multi-UAV formation model with a fixed undirected communication topology, the optimal consensus control protocol incorporating an obstacle avoidance cost function is introduced. This addresses the limitations of APF-based obstacle avoidance while simultaneously managing multi-UAV formation control. Training interactions in randomly generated unknown obstacle environments are conducted using Random Search for Hyperparameter Optimization (RSHO). Combined with the evaluation model, select the optimal solution of the consensus performance index, control consumption performance index, and obstacle avoidance performance index parameters of the multi-UAV formation control system. Furthermore, a virtual repulsive potential field is designed for each UAV to prevent inter-UAV collisions during obstacle avoidance. Simulation results show that the improved APF (IAPF) with optimal consensus control effectively overcomes the limitations of conventional APF. It achieves multi-UAV formation obstacle avoidance control in unknown environments and avoids the phenomenon of inter-UAV collisions during the obstacle avoidance process while maintaining formation integrity, accelerating formation reconfiguration and convergence, reducing consensus consumption and control loss due to obstacle avoidance, shortening mission time, and enhancing obstacle avoidance efficiency, highlighting the superiority of multi-UAV formation obstacle avoidance.

In response to safety concerns pertaining to multi-UAV formation flights, a novel obstacle avoidance method based on an Improved Adaptive Artificial Potential field (IAAPF) is presented. This approach enhances UAV obstacle avoidance capabilities by utilizing segmented attraction potential fields refined with adaptive factors and augmented with virtual forces for inter-UAV collision avoidance. To further enhance the control and stability of multi-UAV formations, a Sliding Mode Control (SMC) method is integrated into the IAAPF-based obstacle avoidance framework. Renowned for its robustness and ability to handle system uncertainties and disturbances, the SMC method is combined with a feedback control system that utilizes inner and outer loops. The outer loop generates the desired path based on the leader’s state and control commands, while the inner loop tracks these trajectories and adjusts the follower UAVs’ motions. This design ensures that obstacle feedback is accounted for before the desired state information is received, enabling effective obstacle avoidance while maintaining formation integrity. Integrating leader-follower formation control techniques with SMC-based multi-UAV obstacle avoidance strategies ensures the effective convergence of the formation velocity and spacing to predetermined values, meeting the cooperative obstacle avoidance requirements of multi-UAV formations. Simulation results validate the efficacy of the proposed method in reaching otherwise unreachable destinations within obstacle-rich environments, while ensuring robust collision avoidance among UAVs.

With the networking of four Global Navigation Satellite Systems, the combination of multi-constellation applications has become an inevitable trend, and there will be more and more visible satellites that can be participated in ship positioning. However, the computational complexity increases sharply, which greatly improves the load capacity of the receiver’s data processor and reduces the output frequency of the positioning result. To achieve the balance between positioning accuracy and computational complexity, a new fast satellite selection algorithm based on both of geometry and geometric dilution of precision contribution is proposed. Firstly, this article analyzes the geometry characteristics of the least visible satellites has minimum geometric dilution of precision that meet the positioning requirements and makes clear the layout of their elevation angles and azimuth angles. In addition, it derives the relationship of geometric dilution of precision and the visible satellites layout and gets geometric dilution of precision contribution of each satellite. Finally, based on the observation data of JFNG tracking station of the Multi-Global Navigation Satellite System (GNSS) Experiment trial network, the positioning error and the elapsed time of GPS/Beidou Satellite Navigation System and GPS/Beidou Satellite Navigation System/Russian Global Orbiting Navigation Satellite System (GLOANSS) are compared. Simulation results show that the algorithm solves the problem that there are a lot of matrix multiplications and matrix inversions in the traditional satellite selection algorithm, and the new algorithm can reduce computational complexity and increase receiver processing speed.

With the considerable increase of visible satellites for positioning, the fault detection and identification performance of Range Consensus (RANCO) algorithm for Receiver Autonomous Integrity Monitoring (RAIM) will significantly be improved. However, the calculation amount of RANCO algorithm will exponentially increase for the sharp addition of visible satellite subsets. This paper proposes a modified RANCO algorithm based on genetic algorithm (GA-RANCO) for RAIM to inhibit the exponentially expanded calculation amount. To reduce the calculation amount in searching the optimal minimal necessary subset (MNS), the preselection step is developed to speed up the convergence process of GA-RANCO. It is executed to exclude the chromosome-represented MNS for which the count of faulty satellites will exceed the upper limit of independent simultaneous satellite faults to be monitored. Mathematical simulations are introduced to determine the GA parameters, and simulation experiments under different schemes are designed to evaluate the performance of GA-RANCO algorithm. Results illustrate that the time consumption under a large number of visible satellites of GA-RANCO is much lower than that of RANCO and the faulty detection and identification performance of GA-RANCO is the same as that of RANCO.

Receiver Autonomous Integrity Monitoring (RAIM) method is an effective means to provide integrity monitoring for users in time. In order to solve the misjudgment caused by the interference of gross error to the least squares algorithm, this paper proposes a RAIM method based on M-estimation for multiconstellation GNSS. Based on five programs, BDS, GPS/BDS, and GPS/BDS/GLONASS at the current stage, the future Beidou Global Navigation Satellite System, and the future GPS/BDS/GLONASS/Galileo system, the new RAIM method is compared with the traditional least squares method by simulation. The simulation results show that, with the increase of constellations, RAIM availability, fault detection probability, and fault identification probability will be improved. Under the same simulation conditions, the fault detection and identification probabilities based on M-estimation are higher than those based on least squares estimation, and M-estimation is more sensitive to minor deviation than least squares estimation.