Autonomous exploration in unknown environments is a critical capability for multi-UAV systems. However, existing methods often suffer from unbalanced task allocation, low exploration efficiency, and unstable paths, especially in large-scale and complex scenarios. To address these challenges, this paper presents an adaptive coordination exploration approach for multi-UAV systems. In the proposed approach, dynamic region allocation, entropy-guided local planning, and direction-consistent frontier selection are integrated to achieve efficient and collaborative exploration. The system first partitions the environment adaptively based on workload and regional complexity. It then prioritizes high-information-value areas for exploration. Directional constraints are further applied to improve path continuity and reduce turning redundancy. Extensive experiments in indoor maze and pillar environments, and outdoor forest and urban environments demonstrate that the proposed approach outperforms state-of-the-art baselines. Furthermore, ablation studies validate the necessity and complementarity of each module. This work provides a practical and efficient solution for multi-UAV exploration in structured and unstructured environments.

Deep-learning-based object detection methods have achieved significant results in unmanned aerial vehicle (UAV) crop seedling image detection. However, when there are differences in the shape characteristics and sizes of seedlings within datasets, the performance of the detector tends to decrease. Existing methods typically rely on specific datasets, ignoring the problem of feature disparities caused by complex and variable field environments. In this letter, a cotton seedling detection framework based on cross-scale feature enhancement (CFE) is presented. CFE reconstructs features through multilevel feature aggregation (MFA) and enhances the reconstructed feature layers using global contextual dependencies extracted by transformer encoder, enabling the sharing of long-range dependency information across different feature spaces. Furthermore, a fuzzy dynamic weighted loss (FDWLoss) strategy is proposed to balance the targets for difficult-to-identify in the training process. Experimental results demonstrate a significant improvement in detection performance and generalization ability on six datasets of the proposed model, which is particularly suitable for cotton seedling detection in various field environments.

Fireworks algorithm is a newly risen and developing swarm intelligence algorithm, the performance of which is determined by the tradeoff between exploration and exploitation. How to develop a satisfactory weight for exploration and exploitation is an interesting and challenging work. In this paper, the landscapes of optimization problem are firstly analyzed, and then a new sparks explosion strategy is designed to represent and mine the landscape information. Moreover, the exploration and exploitation coexist in the improved fireworks algorithm, which can automatically adjust the search strategies according to landscape structure. Finally, numerical experiments are performed for the algorithms investigations, performance analysis, and comparisons. The simulation results indicate that the proposed algorithm has a significant performance on all the test functions and can achieve the global minimum for most test functions.

Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved.

Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.

In this paper, we derive the Chernoff bound for linear-quadratic (LQ) receivers and discuss its applications in CDMA fading channels with uncertainty. A detailed derivation of the Chernoff bound is given and the other bounds related to the J-divergence and the Bhattacharyya distance are computed and compared to the Chernoff bound. Simulation results verify that the Chernoff bound is more closely related to the observed bit error probability even on channels for which the additive noise is not strictly Gaussian, simulations also reveal that the Bhattacharyya distance is potentially a good cost function candidate for receiver design and signal selection.