Operating large multi-reservoir systems is critical for effective water allocation, hydropower generation, and economic development. However, conventional robust planning-based methods scale poorly beyond single-reservoir or cascaded systems due to computational intractability. Addressing inter-reservoir relation extraction and coordination under conflicting objectives, uncertain inflows, and complex network couplings therefore remains an open challenge. To tackle this problem, we introduce the multi-agent reservoir cooperative operation (MARCO) environment, which integrates multiple objectives, heterogeneous topology, and stochastic inflows in a unified framework. An algorithm is then designed to construct a multi-relational directed acyclic graph (MR-DAG) that encodes the underlying topology through coupled objectives and entity relations. Building on this representation, we propose the multi-agent relational directed acyclic graph transformer (MAR-DAGT), a reinforcement learning algorithm that performs typed message passing for efficient feature extraction and employs acyclic decision-making to exploit causal structure for improved credit assignment. Extensive experiments on MARCO show that MAR-DAGT consistently outperforms other MARL and optimization-based baselines in terms of objective satisfaction and robustness to inflow uncertainty.

Multi-robot task planning (MRTP) at scale in robotic mobile fulfillment systems (RMFS) remains a challenge due to the curse of dimensionality and complex dynamic properties. Aiming to solve these challenges, we construct an end-to-end scalable multi-robot task planner capable of scaling to large-scale systems by learning hierarchical planning policies. In this planner, we design a centralized hierarchical temporal task planning framework to mitigate the curse of dimensionality while ensuring timely dynamic response. Following this framework, we propose a novel cycle-constrained asynchronous temporal graph (CycATG) to provide foundation for modeling the system dynamics. Based on the graph representation, we formulate the MRTP problem as a semi-Markov decision process (SMDP) that focuses solely on critical interaction points to improve computational and sampling efficiency. The policies in SMDP are parameterized via a hierarchical temporal attention network with temporal embedding layers to enhance spatio-temporal feature extraction. Additionally, the decoder masks in this network naturally ensure that the generated actions strictly satisfy the required dynamic hard constraints. The above hierarchical policies are jointly optimized using an efficient hierarchical REINFORCE with rollout counterfactual baseline method. To further enhance generalization performance on unlearned instances while preventing catastrophic forgetting, we extend it with region expansion curricula. Experiments demonstrate that our planner outperforms state-of-the-art methods on different MRTP instances across simulated and real-world RMFS. It successfully scales to instances with up to 200 robots, 1000 retrieval racks on unlearned maps while maintaining performance advantages.

Cross-view geo-localization aims at determining the geographic location of a query image by matching the reference images. The matching pairs can be captured from diverse perspectives, such as those from satellites and drones. Most existing methods are supervised that require input of location-labeled images or matched and unmatched image pairs for training, resulting in high labor costs. Moreover, current unsupervised methods perform instances matching directly between different perspectives with dramatic discrepancies, resulting in poor performance. To address these issues, this paper proposes a novel matching and alignment framework from coarse instance-cluster level to fine intermediate instance level for unsupervised cross-view geo-localization. We first introduces cluster-based contrastive learning, assigning pseudo-labels to the instances and generate clusters within each view. Then we design a cross-view location alignment module that fully exploits the feature relationships between instances and clusters for intra- and inter-views. Finally, we design an intermediate state transition module that facilitates further alignment between views by constructing intermediate states and bringing both views closer to the intermediate domain simultaneously. Extensive experiments demonstrate that our method surpasses state-of-the-art unsupervised cross-view geo-localization methods and even achieves comparable performance to state-of-the-art supervised methods.

In this paper, a new Messenger Routing Problem (MRP) is studied, which is motivated by Unmanned Aerial Vehicles (UAVs) accessing Unmanned Ground Vehicles (UGVs) to deliver information in UAV-UGV coordination systems. The objective is to minimize the longest path among multiple messengers, ensuring fast and reliable information transmission. Two key challenges arise in tackling this problem. First, the targets are moving, incurring the travel cost between targets to vary with the travel process. Second, the messengers accessing the neighborhood of targets needs to satisfy the communication time constraint. Based on the idea of decoupling, a two-phase planner is proposed to sequentially determine global access sequence and optimize local access locations. In the first phase, a motion prediction module is introduced in the Adaptive Large Neighborhood Search (ALNS) framework to deal with the dynamic characteristics of MRP. In the second phase, an efficient bisection sampling method based on the prediction points is proposed to obtain a shorter access path while satisfying the communication time constraint. Finally, the effectiveness and efficiency of the proposed method are demonstrated by performance evaluation and comparison with the state-of-the-art algorithms.

Distributed stochastic optimization (DSO) with local set constraints and coupled inequality constraints over a multiagent network is considered in this article. Usually, such problems are tackled by projected primal-dual methods, which require expensive projection operations when set constraints are complicated. In this context, this article focuses on the Frank-Wolfe (FW) framework, which provides computational simplicity by avoiding expensive projection operations, for solving DSO with local set and coupled inequality constraints. By combining recursive momentum and weighted averaging, this article proposes a distributed stochastic FW primal-dual algorithm (DSFWPD), which is the first stochastic FW solver for DSO problems with coupled constraints. The proposed algorithm achieves zero constraint violation on average with a sublinear decay of the optimality gap over a directed and time-varying network. The efficacy of DSFWPD is demonstrated by several numerical experiments.

Deployment optimization of 3-D sensor networks is essential for the overall cost of the system and the downstream tasks performance. The key of establishing realistic deployment is twofold: a high-fidelity mathematical programming model and an efficient algorithm for solving it. In this paper, we revisit the 3-D sensor networks deployment and present a mixed-variable optimization problem (MVOP) which jointly considers the discrete subset selection decision, continuous orientation decision, and decisionmaking under uncertainty. Based on the proposed real-world application, we innovatively design a mixed-variable correlation-aware genetic algorithm as the solver. Different from mainstream two-partition methods in MVOP, our algorithm captures the problem-specific features of deployment optimization and introduces a correlation-aware search paradigm which interactively updates the discrete and continuous decision variables. On the one hand, we update the discrete part (i.e., subset selection of candidate locations) first and then optimize the continuous part (i.e., sensor orientation parameters). On the other hand, we customize a heuristic mechanism to start with continuous part to identify the suitable discrete part. Experiments demonstrate that our approach can improve the performance of small-scale and large-scale scenarios of deployment by up to 55.7% and 56.4%, respectively, compared to state-of-the-art MVOP algorithms.

Facility location is a crucial aspect of spatial optimization with broad applications in urban planning. Specifically, the multi-period problem involves spatial and temporal information, making it challenging to solve. Existing research mainly focuses on heuristic methods, which depend on complex hand-crafted techniques. In this paper, we propose a novel method based on Deep Reinforcement Learning (DRL) to solve pk-median Dynamic Location Problem (DLP-pk). Different from classical heuristic methods, our method avoids intricate designs and considers the temporal impacts of decisions. We are the first to apply DRL to the multi-period facility location problem. Our method adopts the encoder-decoder architecture and utilizes a specialized structure to capture the temporal features across different periods. On the one hand, we introduce the Gated Recurrent Units (GRU) to encode temporal information, including dynamic coverage and dynamic costs. On the other hand, we design an attention-based decoder that allows the model to capture long-term dependencies in decision-making. Experimental results from small-size to large-size demonstrate that our method can quickly provide high-quality solutions without relying heavily on expert knowledge, offering opportunities for efficiently solving multi-period facility location problems. Additionally, our method is up to two orders of magnitude faster than the exact solver Gurobi and demonstrates great generalization abilities.

Most RGB-D-based SLAM methods assume texture-rich environments, making them susceptible to significant tracking errors or complete failures in the absence of texture features. Moreover, many existing methods encounter substantial rotation estimation errors, leading to long-term drift in tracking. This paper proposes a novel structured-constrained RGB-D SLAM method (STL-SLAM) for texture-limited environments. Compared to the existing methods, STL-SLAM can deal with environments without abundant texture information and significantly reduce long-term drift caused by rotation estimation errors. We assess the distribution complexity of pixels in an image by calculating the information entropy and pre-processing accordingly. We also present an efficient Manhattan Frames (MF) detection strategy based on orthogonal planes and lines. If MF is detected, we decouple rotation and translation, estimate drift-free rotation based on the Manhattan World (MW) coordinate system, and then estimate translation by minimizing the re-projection error of point, line, and plane features. In non-Manhattan Frames, the 6-DoF pose estimation is performed holistically, with the incorporation of structural constraints of parallel and perpendicular planes, as well as parallel and vertical lines, into the optimization process. Finally, we evaluate our method on public datasets and in real-world environments, which shows that our proposed method achieves superior performance compared to its counterparts.

Visual sensor networks (VSNs) is a key enabling technology for the development of smart cities. Recent years have witnessed great success in the mechanism design, modeling, and optimization techniques for VSNs. In this paper, we revisit the 3-D heterogeneous VSNs for urban security and design a novel spatial optimization framework to optimize the coverage comprehensively. The proposed framework bridges the computational tools with high performance in spatial geography, structured mathematical models, and modern heuristic algorithms, resulting in an emerging multidisciplinary research paradigm. Specifically, a problem-specific improved genetic algorithm combined with local search is embedded as the solver. Computational results through multiple scenarios of various scales show that the proposed algorithm outperforms the traditional solution approaches. Real-world case studies in Beijing, China are also presented and visualized to illustrate further the optimized configurations of VSNs as well as provide some operational insights for decision-makers in smart cities.

Implanted devices are increasingly used in chronic disease monitoring, but face challenges in energy autonomy. This article presents a novel wireless power transfer (WPT) method for self-sustained medical implants using Halbach array-based magnetic plucking and piezoelectric transduction. The wearable-implantable coupled system consists of a piezoelectric receiver within the implant to receive power and a near-field magnetic power transmitter as a wearable device. To deliver power over greater distances through the human body, the transmitter features a rotating magnetic Halbach array powered by a miniature motor, or by human motion, to generate an alternating magnetic field. The use of low-frequency rotating magnetic fields periodically excites a cantilevered piezoelectric beam with a tip magnet to realize WPT. A theoretical model that includes magnetic coupling, piezoelectric transduction and receiver beam dynamics has been established to study the electro-magneto-mechanical dynamics of this WPT system. The effectiveness of the Halbach array for extended power transfer is examined through theoretical modeling and numerical simulation, showing a 37.2% enhancement of the magnetic forces. A prototype was also fabricated and tested to examine the WPT performance. The established wireless power link can provide sufficient power ($\sim 32~\mu $W) over a large transmission distance (22 mm), providing a potential battery-free solution for the self-sustained Internet of Implanted Things (IoIT) for personalized healthcare.

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents. This study aims to address the problem of dynamic construction of mission groups under new requirements. Agents are heterogeneous, and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored. In our method, a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions. The degree of matching between the mission requirements and the group’s capabilities, and the communication cost of group formation are used as indicators to evaluate the quality of the group. The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations. The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.

The sparrow search algorithm (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal optimization problems. Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced sparrow search algorithm (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an adaptive parameter control strategy is designed to accommodate an adequate balance between exploration and exploitation. Finally, a hybrid disturbance mechanism is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering optimization problems. The results demonstrate the superiority of the MSSSA in addressing practical problems.