The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain-such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that-compared with the existing methods based on APF-the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios. 

/pdf/piecewise-potential-field-based-path-planning-method-for-17p7851v.pdf

Piecewise-potential-field-based path planning method for fixed-wing UAV formation

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. 

Research on Multi-UAV Obstacle Avoidance with Optimal Consensus Control and Improved APF

This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location Mention Disambiguation (LMD). The task aimed to evaluate the ability of automated systems to resolve word ambiguity and identify locations mentioned in Arabic text. We provided participants with novel datasets, including a sense-annotated corpus for WSD, called SALMA with approximately 34k annotated tokens, and the IDRISI-DA dataset with 3,893 annotations and 763 unique location mentions. These are challenging tasks. Out of the 38 registered teams, only three teams participated in the final evaluation phase, with the highest accuracy being 77.8% for WSD and the highest MRR@1 being 95.0% for LMD. The shared task not only facilitated the evaluation and comparison of different techniques, but also provided valuable insights and resources for the continued advancement of Arabic NLU technologies. 

pdf/arabicnlu-2024-the-first-arabic-natural-language-4fahblqoox0g.pdf

ArabicNLU 2024: The First Arabic Natural Language Understanding Shared
  Task

When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased number of decision variables. Moreover, the complex interrelationships between the decision variables of each UAV add to the challenge of selecting appropriate decision variables. To tackle this problem, we propose a novel collaborative algorithm called CCEA-ADVS. This algorithm reduces the difficulty of the problem by decomposing high-dimensional variables into sub-variables for collaborative optimization. To improve the efficiency of decision variable selection in the collaborative algorithm and to accelerate the convergence speed, an adaptive decision variable selection strategy is introduced. This strategy selects decision variables according to the order of solving single-UAV constraints and multi-UAV constraints, reducing the cost of the optimization objective. Furthermore, to improve computational efficiency, a two-stage evolutionary optimization process from coarse to fine is adopted.Specifically, the Adaptive Differential Evolution with Optional External Archive algorithm (JADE) is first used to optimize the waypoints of the UAVs to generate a basic path, and then, the Dubins algorithm is combined to optimize the trajectory, yielding the final flight path. The experimental results show that in four different scenarios involving 40 UAVs, the CCEA-ADVS algorithm significantly outperforms the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and JADE algorithms in terms of path performance, running time, computational efficiency, and convergence speed. In addition, in large-scale experiments involving 500 UAVs, the algorithm also demonstrates good adaptability, stability, and scalability. 

Multi-UAV Path Planning Based on Cooperative Co-Evolutionary Algorithms with Adaptive Decision Variable Selection

Scalable NUMA-aware persistent B+-tree for non-volatile memory devices

Universal constructions are attractive as they can turn a sequential implementation of any data structure into a concurrent implementation. However, existing universal constructions have limitations, such as imposing high copying overhead, or poor scalability on NUMA systems mainly due to their lack of NUMA-aware design principles. To overcome these limitations, this paper introduces CR, a universal construction that provides highly scalable updates on NUMA systems while offering fast read-side performance. CR achieves NUMA-awareness by utilizing delegation within a NUMA node and a global shared log to maintain the consistency of replicas of data structures across nodes. Using CR does not require expertise in concurrent data structure design. Our evaluation shows that CR has up to 11.2 times better performance compared to a state-of-the-art universal construction CX on our tested sequential data structures. To demonstrate the effectiveness and applicability of CR, we have applied CR to an in-memory database system. The database shows up to 18.1 times better performance compared to the original version.

A Universal Construction to implement Concurrent Data Structure for NUMA-muticore

Fixed-wing UAV Kinematics Model using Direction Restriction for Formation Cooperative Flight

Delegation is the highly efficient solution for parallel synchronization. However, the existing the-state-of-the-art delegation locks offer good performance at the cost of occupying computing cores under moderate contention, but exhibit sub-optimal single-thread performance and non-scalable performance under no and high contention, respectively. In this paper, we present a fissionable lock, called FTSD, which consists of two underlying locks: a TTS lock for lock stealing, which serves as a fast path, and a NUMA-aware delegation lock, which offers scalable performance under high contention. Our evaluation shows that FTSD delivers as good or better performance than other state-of-the-art locks.

https://dl.acm.org/doi/pdf/10.1145/3476886.3477518

FTSD: a fissionable lock for multicores

Intelligent warfare has the characteristics of adaptive construction and dynamic reorganization, which is different from traditional warfare. The different warfare modes lead to the change of simulation modeling, and then lead to the change of simulation platform supporting modeling and simulation warfare. Based on the analysis of four kinds of simulation platform based on reflection memory network, distributed environment, parallel computing environment and middleware, combining with the characteristics of intelligent warfare application, a kind of implementation framework of simulation platform for intelligent warfare is proposed, and the organization mode of intelligent algorithms under this framework is given. This study has important theoretical significance and reference value.

Research on Simulation Platform for Intelligent Warfare

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