Search Results (294)

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning. Full article

Navigating and planning optimal paths for resource delivery algorithms poses significant physical and technical challenges in urban areas, primarily due to the limitations of existing infrastructure. As smart cities continue to develop, the importance of these algorithms becomes increasingly evident. The saturation of current urban landscapes exacerbates the complexity of navigating essential resources. Navigating densely connected networks can be intricate and often requires substantial computational resources or additional algorithms, as it can easily transform into an NP problem. Unfortunately, there is a lack of explicit algorithms designed for navigating these networks, resulting in a dependence on heuristic approaches and previous network systems. This reliance can create computational challenges, as navigation in this context typically involves a combinatorial search space. Current advances in Morphological Mathematics (MM) help to model everyday tasks as processes in discrete spaces, which take advantage of the properties offered by the morphological operators. Morphological Shortest-Path-Planning (MSPP) is a recent solution that effectively calculates the optimal trajectory within complex graphs. By utilizing morphological operators, this approach takes into account discrete properties and maps the process as a complete implementation algorithm using integer logic. In larger cities, determining the optimal delivery route and time from a resource center is a common task. This process is influenced by factors such as average speed, travel time, and distance, which generate a complex graph representation of the town, complicating its analysis. This paper presents a strategy for computing and analyzing delivery times by determining the accessibility of reliable paths from a delivery center to potential destinations in dense urban areas. The strategy presented and the use of the MSPP approach are suitable for calculating the time spent delivering and the distance traveled in working journeys. The MSPP approach is found to be nearly $60\%$ more efficient than the reference approach for computing the optimal path in the case study presented. Full article

In current intelligent manufacturing workshops, multi-automated guided vehicle (AGV) systems often face issues such as uneven task allocation, path conflicts, and idle travel, which significantly affect scheduling efficiency. To address these problems, this paper proposes an improved ant colony algorithm that collaboratively optimizes task allocation and path planning by integrating path costs and AGV task execution capabilities. The algorithm utilizes shortest-path planning results to optimize task allocation priorities, achieving synchronized optimization of task scheduling and path planning. Based on this, a multi-objective scheduling model is constructed with the goal of minimizing task completion time, idle travel distance, and total travel distance. The results show that the method effectively shortens task completion time and significantly improves scheduling efficiency, verifying its feasibility for application in intelligent manufacturing workshops. Full article

The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient paths, which is difficult for traditional algorithms due to the time-varying environment and the highly coupled multi-system dynamics of the airship. This study proposes a deep reinforcement learning algorithm, termed reward-prioritized Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (RPL-TD3). The method incorporates an LSTM network to effectively capture the influence of historical states on current decision-making, thereby improving performance in tasks with strong temporal dependencies. Furthermore, to address the slow convergence commonly seen in off-policy methods, a reward-prioritized experience replay mechanism is introduced. This mechanism stores and replays experiences in the form of sequential data chains, labels them with sequence-level rewards, and prioritizes high-value experiences during training to accelerate convergence. Comparative experiments with other algorithms indicate that, under the same computational resources, RPL-TD3 improves convergence speed by 62.5% compared to the baseline algorithm without the reward-prioritized experience replay mechanism. In both simulation and generalization experiments, the proposed method is capable of planning feasible paths under kinematic and energy constraints. Compared with peer algorithms, it achieves the shortest flight time while maintaining a relatively high level of average residual energy. Full article

The field of autonomous robotics is progressing rapidly, with research moving toward developing systems capable of moving without direct human control and learning without human intervention. One of the problems requiring an efficient and sustainable solution is ensuring the smooth and safe navigation of robots between obstacles. In this study, a new path planning approach is developed, integrating particle swarm optimization (PSO) and artificial potential field (APF) algorithms to assist the mobile robot in navigating an area with static and dynamic obstacles. The robot moves independently while routing dynamically and avoiding obstacles. To evaluate its adaptive ability to a changing environment, we continuously calculate the shortest distance between two points and dynamically adjust the path to avoid obstacles during replanning, path recalculation, and robot position adjustment to ensure efficient and safe navigation. Different scenarios are tested to evaluate our approach, including different environmental conditions and obstacle configurations. Experimental results show that our method reduces the path length by 18%, the obstacle avoidance efficiency by 90%, and the success rate by 85% in dynamic environments. In addition, PSO-APF reduces computation time, demonstrating better capacity and efficiency. Full article

Since efficient path planning technology is the key to the safe and autonomous navigation of autonomous ground robots, and in the complex and asymmetrically distributed land environment, the existing path planning and obstacle avoidance technologies seem somewhat inadequate. Since efficient path planning technology is key to the safe and autonomous navigation of autonomous ground robots, an advanced double Q-learning algorithm based on self-supervised prediction and curiosity-driven exploration is proposed. The algorithm reduces the risk of overestimation and bootstrapping by adjusting the calculation method of the target Q value and optimizing the network structure. In addition, a priority experience replay is introduced to set the priority for the data in the experience pool, thereby increasing the probability that better data is extracted. Experience pool data with fewer training times can be used more effectively. Adding the curiosity network to the original neural network, each state is given an overall reward when performing diverse actions. This method enhances the exploration of unmanned ground mobile robots and can independently select the shortest path to the endpoint. In complex environments, compared with the Sparrow Search Algorithm, Dung Beetle Optimization Algorithm, and Particle Swarm Optimization Algorithm, the results of the proposed algorithm are reduced by 18.07%, 7.91%, and 5.56%, respectively. Therefore, it could better cope with the challenges brought by complex environments and solve the problem that the algorithm cannot converge in complex environments. Full article

Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and Mapping (SLAM). To minimize distortions in LiDAR scans caused by ground irregularities, real-time tilt correction was implemented based on IMU feedback. Furthermore, the path planning module was improved by modifying the Rapidly-Exploring Random Tree (RRT) algorithm. The enhanced RRT generated smoother and more efficient trajectories with quantifiable improvements: the average shortest path length was 2.26 m, compared to 2.59 m with conventional RRT and 2.71 m with A* algorithm. Tracking performance also improved, achieving a root mean square error of 0.890 m and a maximum lateral deviation of 0.423 m. In addition, yaw stability was strengthened, as heading fluctuations decreased by approximately 7% relative to the standard RRT. Field results validated the robustness and adaptability of the proposed system under real-world agricultural conditions. These findings highlight the potential of LiDAR–IMU sensor fusion and optimized path planning to enable scalable and reliable autonomous navigation for precision agriculture. Full article

Path planning is a complex task in robotics, requiring an efficient and adaptive algorithm to find the shortest path in a dynamic environment. The traditional path planning methods, such as graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms, have limitations in computational efficiency, real-time adaptability, and obstacle avoidance. To address these challenges, hybrid path planning algorithms combine the strengths of multiple techniques to enhance performance. This paper includes a comprehensive review of hybrid approaches based on graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms. Also, this article discusses the advantages and limitations, supported by a comparative evaluation of computational complexity, path optimization, and finding the shortest path in a dynamic environment. Finally, we propose an AI-driven adaptive path planning approach to solve the difficulties. Full article

Optimizing tourist routes is a critical challenge in smart tourism, which aims to enhance the visitor experience while optimizing practical parameters. However, traditional routing algorithms often fail to provide personalized and efficient itineraries in complex real-world environments. This study aims to develop a hybrid framework that integrates Simulated Annealing for global route optimization with the A algorithm* for accurate local pathfinding, leveraging geographic data from OpenStreetMap. The proposed method computes the shortest paths between all Points of Interest using A*, constructing a comprehensive distance matrix, and applying Simulated Annealing to determine the most efficient visiting sequence. The framework was evaluated in the Old Medina of Fez, Morocco, demonstrating its effectiveness in generating realistic and efficient itineraries. Compared to alternative strategies such as Genetic Algorithms, the hybrid approach achieves superior computational efficiency and produces better routes in terms of travel distance. These findings highlight the practical applicability of the framework as a modular service for smart tourism applications, offering tourists and tourism platform developers a scalable solution for personalized and sustainable itinerary planning. Full article

Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of adaptive autonomy for path planning. This approach allows the system to autonomously select the best path-planning strategy. The technique utilizes dynamic programming with an adaptive memory size, leveraging a cellular decomposition technique to divide the map into convex cells. The path is divided into three segments: the first segment connects the starting point to the center of the starting cell, the second segment connects the center of the goal cell to the goal point, and the third segment connects the center of the starting cell to the center of the goal cell. Since each cell is convex, internal path planning simply requires a straight line between two points within a cell. Path planning uses an improved $A^{\ast }$ (I-$A^{\ast }$) algorithm, which evaluates the feasibility of a direct path to the goal from the current position during execution. When a direct path is discovered, the algorithm promptly returns and saves it in memory. The memory size is proportional to the square of the total number of cells, and it stores paths between the centers of cells. By storing and reusing previously calculated paths, this method significantly reduces redundant computation and supports long-term sustainability in mobile robot deployments. The final phase of the path-planning process involves pruning, which eliminates unnecessary waypoints. This approach obviates the need for repetitive path planning across different scenarios thanks to its compact memory size. As a result, paths can be swiftly retrieved from memory when needed, enabling efficient and prompt navigation. Simulation results indicate that this algorithm consistently outperforms other algorithms in finding the shortest path quickly. Full article

The cable crane is the core hoisting equipment for high arch dam construction, and its hoisting trajectory is critical for both operational efficiency and safety. However, current trajectory planning does not adequately consider the underactuated characteristics of the cable crane. For instance, sudden stops or abrupt changes in direction can easily induce large swings of the bucket, causing safety risks and equipment wear. To address this issue, this paper developed a trajectory planning model for obstacle avoidance with smooth transitions in cable crane hoisting for arch dams and solved the high-dimensional optimization problem using a path–velocity decoupling strategy. First, a shortest path with geometrical conciseness and free collision was generated based on an improved A* algorithm to reduce the frequency of directional changes. Next, for different hoisting scenarios, segmented S-curve and polynomial velocity functions were proposed to ensure smooth velocity transitions. Then, an orthogonal experimental design was employed to generate a cluster of candidate trajectories that meet kinematic constraints, from which the optimal trajectory was selected using a multi-objective evaluation function. The results demonstrate that the motion trajectory planned using the proposed method is notably smoother. Compared with the traditional trapezoidal velocity method, it reduces the maximum swing amplitude of the bucket by 40.78% at a modest time cost. In real-time obstacle avoidance scenarios, the approach outperforms emergency-stop strategies, reducing the bucket’s maximum swing amplitude by 30.48%. This work will provide a reference for engineers to optimize the trajectory of large lifting equipment in construction fields such as high arch dams and bridges. Full article

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article

Research in on-orbit processing enables edge AI deployment over satellite optical networks. However, these operations induce frequent battery discharge cycles, particularly depth-of-discharge (DoD) events, which accelerate degradation and curtail satellite longevity. To address this, we propose green task-aware routing planning (GreenRP), a task-aware routing framework that achieves sustainable edge AI through dynamic task offloading and discharge-resilient path orchestration. GreenRP employs a novel battery aging model explicitly coupling DoD effects with laser inter-satellite link dynamics under AI workloads, enhancing system sustainability. Comprehensive evaluation on a 1152-satellite constellation demonstrates that GreenRP extends network lifetime by 176% over shortest-path routing while meeting latency and completion rate targets. This work enables reliable edge AI via sustainable satellite resource utilization. Full article

NetworkX is a free Python library for graphs and networks and is used in many applications and projects to find the shortest path in path planning scenarios. For dense graphs, the library provides the Floyd–Warshall algorithm for shortest paths and the A* (“A-Star”) algorithm for shortest paths and path lengths. However, several extensions have been proposed to improve the A*, but they are not included in the library. In this context, this paper presents a set of implementations improving the A*, such as the IDA*, D* Lite, SMA*, Bidirectional A* and RTA*. The goal or challenge is to address the limitations of the A* in specific scenarios, such as searching for an optimal path repeatedly or when confronted with memory limitations, as exemplified by the NetworkX library. To do this, we first review the literature of the usage and general application of NetworkX in different domains of applicability and then explore their usage in a shortest path context. By reviewing and validating the usage of A* and extensions in Python using the NetworkX framework, the implementations were submitted to the network environment validation and passed the tests. We have also done the benchmarking of the A*, comparing it with the new ones, and concluded the better efficiency of the A* extensions in tri-objective scenario parameters (length, cost and toll). Despite the extensive utilisation of A* and its notable efficacy in identifying optimal paths, its performance is suboptimal in specific scenarios, such as when confronted with memory constraints and dynamic environments. Almost every algorithm outperformed or matched the A* in the fields that were developed to have an advantage, demonstrating the quality and robustness of the implemented algorithms. As a contribution and to foster further research in this shortest path specific context field, the dataset and Python code of the algorithms are available in a GitHub opensource repository. Full article

Optical networks serve as the backbone of modern communication, requiring statistical analysis and modeling to optimize performance, reliability, and scalability. This review paper explores statistical methodologies for analyzing network characteristics, dimensioning, parameter estimation, and cost prediction of optical networks, and provides a generalized framework based on the idea of convex areas, and link length and shortest path length distributions. Accurate dimensioning and cost estimation are crucial for optical network planning, especially during early-stage design, network upgrades, and optimization. However, detailed information is often unavailable or too complex to compute. Basic parameters like coverage area and node count, along with statistical insights such as distribution patterns and moments, aid in determining the appropriate modulation schemes, compensation techniques, repeater placement, and in estimating the fiber length. Statistical models also help predict link lengths and shortest path lengths, ensuring efficiency in design. Probability distributions, stochastic processes, and machine learning improve network optimization and fault prediction. Metrics like bit error rate, quality of service, and spectral efficiency can be statistically assessed to enhance data transmission. This paper provides a review on statistical analysis and modeling of optical networks, which supports intelligent optical network management, dimensioning of optical networks, performance prediction, and estimation of important optical network parameters with partial information. Full article