This paper presents IB-DARP (Iteration Balancing—Divide Areas Routing Problem), an enhanced multi-vessel cooperative mission and path planning method designed to address the limitations of traditional approaches, including uneven task allocation, workload imbalance, and path conflicts. The proposed method integrates four key mechanisms to improve planning robustness and computational efficiency. A historical data mining mechanism is first employed to extract stable navigation patterns from accumulated vessel trajectories and construct a high-confidence maritime route network. Based on this network, a precomputation mechanism significantly reduces planning-stage computational complexity by calculating essential inter-node distances in advance. A heading-aware partitioning mechanism further decomposes the multi-vessel planning problem into tractable single-vessel subproblems, while an iterative auction–equilibrium mechanism dynamically adjusts task assignments to enhance global load balance and suppress conflicts. To evaluate the effectiveness of IB-DARP, comprehensive ablation studies and large-scale scenario experiments were conducted, demonstrating its advantages in mission allocation, conflict mitigation, and cooperative path optimization. The results confirm that IB-DARP provides a scalable and efficient solution for multi-vessel cooperative mission planning in complex maritime environments.

Autonomous navigation is a core enabler of smart agriculture, where path planning and trajectory tracking control play essential roles in achieving efficient and precise operations. Path planning determines operational efficiency and coverage completeness, while trajectory tracking directly affects task accuracy and system robustness. This paper presents a systematic review of agricultural robot navigation research published between 2020 and 2025, based on literature retrieved from major databases including Web of Science and EI Compendex (ultimately including 95 papers). Research advances in global planning (coverage and point-to-point), local planning (obstacle avoidance and replanning), multi-robot cooperative planning, and classical, advanced, and learning-based trajectory tracking control methods are comprehensively summarized. Particular attention is given to their application and limitations in typical agricultural scenarios such as open-fields, orchards, greenhouses, and hilly slopes. Despite notable progress, key challenges remain, including limited algorithm comparability, weak cross-scenario generalization, and insufficient long-term validation. To address these issues, a scenario-driven “scenario–constraint–performance” adaptive framework is proposed to systematically align navigation methods with environmental and operational conditions, providing practical guidance for developing scalable and engineering-ready agricultural robot navigation systems.

Cooperative path planning of multiple unmanned aerial vehicles (UAVs) is pivotal for improving mission efficiency and safety in complex scenarios. However, the multi-constraint of UAVs increases the design difficulity of cooperative path planning. To address these issues, a hybrid search behavior-based adaptive grey wolf optimizer (HSB-GWO) is proposed in this work. HSB-GWO incorporates three key innovations: (1) A dimension learning-based hunting (DLH) strategy is employed to enhance population diversity by enabling knowledge exchange between non-leader wolves and their neighbors. (2) Aquila exploration combining expand exploration for global potential region detection and Lévy flight-based narrowed exploration for preventing populations from falling into local optimal solutions is adopted to enrich search behaviors and avoid local optima. (3) An adaptive weight adjustment mechanism is designed for leader wolves (, , and ) to dynamically tune their contribution to offspring generation based on fitness to improve high-quality solution utilization. The search performance of HSB-GWO on the benchmark functions was validated by experiments on the benchmark suites of IEEE CEC 2017 and 2019, in which HSB-GWO outperformed seven comparison algorithms (AO, AOA, CBOA, NOA, GWO, IGWO, and AGWO), with Friedman test confirming its top overall rank (Rank 1). The results of cooperative path planning simulation demonstrate that the high-quality multi-UAV trajectories can be generated by the HSB-GWO to guide UAVs from the start to the destination safely and smoothly with the smallest cost.

Number of vehicles in metropolitan areas is rapidly increasing, leading to worsening traffic congestion. There is an urgent need for the implementation of effective vehicle routing planning (VRP) to increase road traffic efficiency. However, the existing path planning algorithms focus primarily on the optimization of single vehicles, neglecting the correlations in routing demands and the collective behavior of vehicles. To address these issues, this paper proposes evolutionary game-based multivehicle route planning (EG-MVRP). By constructing a vehicle grouping model and applying evolutionary game theory, we analyze the interest conflicts and coordination requirements among different vehicle groups, which leads to the formulation of an optimized cooperative path planning strategy for multiple vehicles. The experimental results demonstrate that EG-MVRP significantly enhances the efficiency of intragroup path planning, alleviates local congestion, and improves the overall operational efficiency of the traffic network by minimizing excessive competition among multiple vehicle groups on the same road sections. In addition, the proposed method offers clear advantages in reducing travel time and fuel consumption. The research presented in this paper offers novel ideas and methods for future traffic management and planning, offering significant practical application value.

Cooperative and Asynchronous Transformer-Based Mission Planning for heterogeneous teams of mobile robots

Issue/ProblemThe wildfire of August 7 and 8, 2024 in the Municipality of Amari highlighted the vulnerability of the area to natural disasters, due to its extensive forest and agricultural coverage, high combustible material, strong winds, and the dispersion of small settlements with difficult access. Factors such as agricultural activities increased the risk of outbreak and spread of the fire. The disaster started in the rural area of Ano Meros and quickly spread to neighboring villages, requiring the evacuation of nine settlements.ResultsThe social consequences were profound: although there were no human losses, plant and animal capital—the main source of income for residents—was destroyed, resulting in psychological stress, social insecurity, and financial difficulties.LessonsThe implementation of the ‘IOLAOS’ Plan proved crucial, with the Municipality, the Regional Unit, the Region, and the Fire Service coordinating effectively. The timely dispatch of 112 messages facilitated safe evacuation. The Deputy Mayor for Social Protection, in cooperation with the ‘Help at Home’ program, the intercity bus service (KTEL), cultural associations, the local Metropolis, and volunteers, ensured the transfer, accommodation, and catering of vulnerable groups, as well as the provision of psychosocial and nursing care. Significant was also the contribution of individuals and private entities that provided food, essential goods, and support.Key messages• The experience highlighted the necessity of a comprehensive action plan, the value and strengthening of cooperation among public, private, and voluntary bodies, as well as alignment with European Civil Protection guidelines (Regulation 1313/2013, rescEU, EFFIS), which enhance prevention and coordination.

Cooperative path planning is recognized as a critical technology for Autonomous Underwater Vehicle (AUV) clusters to execute complex marine operations. Through multi-AUV cooperative decision-making, perception limitations of individual robots can be mitigated, thereby significantly enhancing the efficiency of tasks such as deep-sea resource exploration and submarine infrastructure maintenance. However, the underwater environment is characterized by severe disturbances and limited communication, making cooperative path planning for AUV clusters particularly challenging. Currently, this field is still in its early research stage, and there exists an urgent need for the integration of scattered technical achievements to provide theoretical references and directional guidance for relevant researchers. Based on representative studies published in recent years, this paper provides a review of the research progress in three major technical domains: heuristic optimization, reinforcement and deep learning, and graph neural networks integrated with distributed control. The advantages and limitations of different technical approaches are elucidated. In addition to cooperative path planning algorithms, the evolutionary logic and applicable scenarios of each technical school are analyzed. Furthermore, the lack of realism in algorithm training environments has been recognized as a major bottleneck in cooperative path planning for AUV clusters, which significantly limits the transferability of algorithms from simulation-based validation to real-sea applications. This paper aims to comprehensively outline the current research status and development context of the field of AUV cluster cooperative path planning and propose potential future research directions.

Research on multi-algorithm cooperative path planning and decision-making mechanism for switching operations in substations based on a hybrid optimisation strategy combining A* and DWA

This study comparatively analyzed major nuclear accidents in the United States (Three Mile Island), Japan (Fukushima), and South Korea (Kori & Wolsong) to enhance South Korea's response system amid the growth of nuclear power. The analysis focused on the initial post-accident response, information disclosure, resident evacuation, regulatory independence, and domestic/international cooperation. Based on these findings, the study proposes involving the National Fire Agency in the response framework, adopting and supplementing IAEA standards, developing measures tailored to the Korean context, and strengthening cooperation plans.

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems.

This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, a three-layer cooperative search framework to achieve a stronger balance between exploration and exploitation, and a dynamic adaptive mechanism with t-distribution re-exploration to reinforce both global escaping and local refinement. On the CEC2017 benchmark suite, IWMA demonstrates clear superiority over seven representative algorithms, delivering the best results on 27 out of 29 functions by best, 25 by mean, and 23 by standard deviation in 30 dimensions, and on 25, 18, and 18 functions, respectively, in 50 dimensions. Compared with other migration-based optimizers, its average rank improves by more than 30 percent, while runtime analysis shows only a small additional overhead of 7 to 12 percent. These outcomes, supported by convergence curves, boxplots, radar charts, and Wilcoxon tests, confirm the effectiveness of the proposed improvements. In six multi-UAV path planning scenarios, IWMA reduces the average cost by 14.5 percent compared with WMA and achieves up to 32.1 percent reduction in the most complex case. Overall, its average cost decreases by 27.4 percent across seven competitors, with a 23.6 percent improvement in the best solutions. These results demonstrate that the proposed modifications are effective, enabling IWMA to transfer its performance gains from benchmark tests to practical multi-UAV cooperative mission planning, where it consistently produces safer and smoother trajectories under complex constraints.

This article examines the current state of cooperation between the Republic of Kazakhstan and various foreign nations in the military domain. According to the authors, in recent years, amid global military-political instability, the primary condition for mitigating adverse consequences is the continued collaboration in the military sector. The study analyzes measures taken to ensure military security,as well as the distinctive features of military activity directions. The objective of this research is to explore the legal foundations of the Republic of Kazakhstan’s military cooperation with international organizations, the Republic of Turkey, and the Russian Federation. During the research, a legal analysis of agreements in the military domain was conducted, and comparative-legal methods were applied. The research is grounded in a range of normative sources, including the Constitution of the Republic of Kazakhstan, the country’s military doctrines, agreements on military cooperation with the Republic of Turkey, military cooperation plans, and various other legal instruments that govern military collaboration. The authors conclude that the establishment of military cooperation with neighboring countries to ensure peaceful coexistence is one of the most crucial functions of any state in guaranteeing military security

To enhance the maneuvering efficiency and safety of the aircraft towbarless towing vehicle (TTV) system, this study presents an optimized path planning method based on an improved artificial potential field (APF) algorithm. First, comprehensive kinematic and dynamic models are established, incorporating both lateral and yaw motions of the TTV system. Second, to mitigate obstacle interference challenges in complex airport environments, the proposed method introduces an innovative relative-distance safety factor and implements a dual-repulsive-force cooperative planning strategy, effectively overcoming the traditional APF algorithm’s limitations regarding goal unreachability and local minima. Furthermore, the integration of Bézier curves ensures curvature continuity in the planned path, thereby maintaining compliance with kinematic constraints. Finally, a constrained-motion TTV simulation model is developed to validate the algorithm’s performance. Simulation results demonstrate that, in static obstacle scenarios, the proposed method successfully enables autonomous path planning, generating smooth and collision-free trajectories. This approach offers a robust solution for ensuring stable and reliable operation of the TTV system in real-world airport environments.

This study investigated the effectiveness of integrating cooperative learning with the FiF App to improve students'English-speaking proficiency during off-class hours. The study's overarching aim was to enhance speaking proficiency in semantics, pronunciation, fluency, and completeness, increase students'interest, and examine their learning experiences.The study employed a mixed-methods approach with a one-group pre-test and post-test design involving 49 Primary English Education students from Lijiang Normal University. The 9-week intervention involved cooperative learning-based lesson plans, and the FiF App. Speaking proficiency was assessed before and after the intervention through the app, while students'interest towards speaking English and experiences were measured using questionnaires and semi-structured interviews.Results showed significant improvement in overall speaking proficiency, with mean scores increasing from 49.57 to 69.96,t(48) = 6.16, p < 0.001. Pronunciation improved from 59.94 to 85.61 (t = -7.48, p < 0.001), and semantics increased from 29.55 to 53.55 (t = 5.59, p < 0.001). Although fluency scores decreased numerically from 78.98 to 64.31, the t-test (t = 4.41, p < 0.001) indicated significant improvement, likely to reflect greater accuracy over speed. Completeness rose slightly from 92.78 to 95.69 while moderate increases in speaking confidence (M = 3.23) and positive perceptions of cooperative learning (M = 3.86). The perceived importance of speaking skills was lower (M = 2.66). Interview data supported these findings, highlighting enhanced motivation, more meaningful peer interaction, and increased opportunities for practice. This study demonstrates the effective combination of cooperative learning and mobile-assisted oral training outside class, encouraging educators to adopt blended models to improve English-speaking proficiency beyond traditional classroom settings.

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section.

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction.

Writing is considered by students as a difficult skill to study. They do not realize that communication can be transferred through writing. Consequently, they write without purpose and in disorder way. They only write what is inside their head down in the paper and ignore what their purpose is, its grammatical and vocabulary use. Group Investigation (GI) is an organizational approach that allows a class to work actively and collaboratively in small groups and enables students to take an active role in determining their own learning goals and processes. GI is interesting so that the students will feel the new atmosphere in classroom and they are interested in learning writing. This study aims to investigate the implementation of GI to enhance the students’ writing ability. This study was conducted by using classroom action research that has two cycles within. The subject of the research was the students of eleventh year SMKN 2 Kediri which consisted of 35 students. The instruments of collecting data were quantitative (writing test) and qualitative data (observation sheet, and field notes). Questionnaire and interview being used by the researcher to know deeply the problem faced by the students then she analyzed them to find the appropriate method. They could follow the writing process from (1) topic selection, (2) cooperative planning, (3) implementation, (4) analysis and synthesis, (5) presentation of final project, and (6) evaluation. Based on the writing test scores, the students’ score kept improving in every test. In the pre-liminary study, there were only 43% of the students who passed the minimum standard score. After giving treatment to the students using GI, there was an enhancement towards the students writing. 91% of the students passed the test in cycle 1 and 100% of the students passed the test in cycle 2. Based on the observation sheet and field notes, it was found that the learning process of writing ran well. The students can be more active in group work, and they can follow the process of writing well-ordered by implementing the steps of GI. The result of the research showed that GI method could enhance students’ achievement in writing instruction text. It was suggested to modify and make the GI method to be more interesting and suitable towards the skill and topic of the study.

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems.

Cooperation among telecom carriers and datacenter providers (DCPs) is essential to ensure the resiliency of network-cloud ecosystems. To enable efficient cooperative recovery in case of traffic congestion or network failures, we introduce a novel, to our knowledge, multi-entity cooperation platform (MCP) for implementing cooperative recovery planning. The MCP is built over distributed ledger technology (DLT), which ensures decentralized and tamper-proof information exchange among stakeholders to achieve open and fair cooperation. We experimentally demonstrate a proof-of-concept DLT-based MCP on a testbed. We showcase a DCP–carrier cooperative planning process and the corresponding recovery in the data-plane, showing the possibility of multi-entity cooperation for quick recovery of network-cloud ecosystems.

Spatial multi-vector and multi-rigid-body obstacle avoidance planning for multi-robot coordinated suspension system.