We address the problem of angle-of-arrival (AOA) target tracking using multiple unmanned aerial vehicles (UAVs) in three-dimensional (3D) space. A distributed 3D AOA target tracking method is ...proposed consisting of a distributed estimator and path optimization algorithm for multiple UAVs. First a novel 3D distributed pseudolinear Kalman filter (DPLKF) is developed to improve the stability of an extended Kalman filter solution. The DPLKF consists of two coupled filters; viz., an xy-DPLKF and a z-DPLKF. The bias problem of the 3D DPLKF is analyzed and a bias reduction method is proposed. A distributed path optimization algorithm is developed subject to communication range constraints and no-fly zones. This algorithm computes UAV waypoints using gradient-descent optimization on the xy-plane and grid search along the z-axis. To improve the tracking performance, the trace of the error covariance matrix is minimized. The properties and effectiveness of the proposed strategy are discussed and validated with simulation examples.
•A novel distributed pseudolinear Kalman filter (DPLKF) is developed for 3D AOA target tracking.•The bias problem of the 3D DPLKF is analyzed and a bias reduction method is proposed.•A distributed path optimization algorithm is developed subject to communication range constraints and no-fly zones.
Intelligent path planning is a significant tool for field of industrial robot. This field has attracted the attention of numerous researchers due to the great market demands, broad application ...prospects, and large potential development. Due to the limitation of neighborhood, the path search by the original A* algorithm is more likely to fail, and the solved path may contain too many local paths. In this study, an improved A* algorithm is proposed to solve the robot path planning problem. The first improvement of the advanced method is the local path between the current node and the goal node, which is planned before the next search in the neighborhood of the current node. And the local path will be adopted directly if it is safe and collisionless. The second advantage of this method is the utilization of post-processing stage to optimize the resulting path, by straightening the local path to reduce the number of local paths as well as the path length. In order to verify the theoretical advantages of the improved A* algorithm, a series of two-dimensional figures of the robot task was presented in this paper. In addition, some comparative experiments in the virtual and real robot manipulator platform are performed to examine the improved A* algorithm. Experimental results show that the search success rate of the improved A* algorithm is higher than the original A* algorithm, along with a shorter and smoother path could be obtained by the improved A* algorithm. Therefore, the success rate of robot path planning and the optimal extent of the robot path are effectively improved by the improved A* algorithm.
•Focus on path planning for industrial robots in complex environments.•A local safety and collisionless path was built between the sampling points by local path planner.•The local path to the goal node is planned before the next search in the neighborhood of the current node.•A post-processing stage used to optimize the resulting path.•Path planning problem is solved by the improved A* algorithm with high search success rate and short length.
Accurately assessing the effectiveness of industrial carbon emission reduction in each province and optimizing the emission reduction path have important practical significance for China's Nationally ...Determined Contribution (NDC) emission reduction achievement targets. This study first evaluates the industry's emission reduction effects across 30 provinces of China. Then, the emission reduction paths of “lagging regions,” which fail to meet the 2030 industrial carbon emission reduction target, are optimized based on the two-dimensional perspective of carbon emission efficiency and emission reduction cost. This study found that (1) China has exceeded its 2020 industrial carbon emission reduction target. There are 9 potential “lagging regions” that failed to meet their 2020 targets, (2) if the current emission reduction rate is maintained, China is capable of exceeding its 2030 industrial carbon emission reduction target, but there are still 11 “lagging regions,” (3) there are clear differences in carbon emission efficiency and shadow price among the “lagging regions,” and (4) under the premise of ensuring feasibility and fairness, the three provinces of Liaoning, Guangxi, and Shaanxi can set strict emission reduction targets, while other “lagging regions” can set flexible targets.
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•Industrial carbon emission reduction status in China's 30 provinces is clarified.•There are 11 industrial carbon emission reduction “lagging regions”.•The emission reduction paths of “lagging regions” are optimized based on the efficiency-cost analysis.•Differentiated targets are used to optimize carbon emission reduction paths.
무인 항공기는 다양한 분야에서 널리 활용되고 있으며, 실시간 경로 재계획은 이들 기기의 안전성과 효율성을 향상하는 핵심 요소이다. 본 논문에서는 RRT*와 LOSPO를 기반으로 한 실시간 경로 재계획 기법을 제안한다. 제안된 기법은 먼저 RRT* 알고리즘을 활용하여 초기 경로를 생성하고, LOSPO를 이용하여 경로를 최적화한다. 또한 최적화된 경로를 ...궤적으로 변경하여 실제 시간과 항공기의 동적한계를 고려할 수 있다. 이 과정에서 환경 변화와 충돌 위험을 실시간으로 감지하고, 필요한 경우 경로를 재계획함으로써 안전한 운행을 유지한다. 이 방법은 시뮬레이션을 통한 실험을 통해 검증되었다. 본 논문의 결과는 무인 항공기의 실시간 경로 재계획에 관한 연구에 중요한 기여할 것으로 기대한다. 또한 이 기법을 다양한 상황에 적용함으로써 무인 항공기의 안전성과 효율성을 향상시킬 수 있다.
Unmanned aerial vehicles are widely used in various fields, and real-time path replanning is a critical factor in enhancing the safety and efficiency of these devices. In this paper, we propose a real-time path replanning technique based on RRT* and LOSPO. The proposed technique first generates an initial path using the RRT* algorithm and then optimizes the path using LOSPO. Additionally, the optimized path can be converted into a trajectory that considers actual time and the dynamic limits of the aircraft. In this process, environmental changes and collision risks are detected in real-time, and the path is replanned as needed to maintain safe operation. This method has been verified through simulation-based experiments. The results of this paper make a significant contribution to the research on real-time path replanning for UAVs, and by applying this technique to various situations, the safety and efficiency of UAVs can be improved.
China has proposed the goals of a carbon peaking target in 2030 and a carbon neutrality target in 2060 to mitigate climate change. In China, Eco-industrial parks (EIPs) are one of the platforms for ...achieving energy conservation and emission reduction. This study aims to investigate the future CO2 emissions reduction potential by evaluating two different types of national EIPs in China. Firstly, we constructed carbon emission inventories for EIP-N in Zhejiang Province and EIP-L in Shandong Province. Subsequently, leveraging these inventories, we developed an integrated model aimed at predicting the carbon emission peaks for both EIPs and discerning the main contributors. Secondly, we took EIP-N and EIP-L as examples and applied the Long-range Energy Alternatives Planning (LEAP) model to establish three different scenarios for analyzing the future trends in carbon emissions. In a Business as usual (BAU) scenario (continue the current energy conservation and emission reduction policies), EIP-L can achieve carbon peaking, while the carbon emissions of EIP-N will continue to increase. In the Emissions control (EMC) scenario (considering the reduction of fossil fuels and the increased use of cleaner energy sources), EIP-N and EIP-L are projected to peak in 2029 and 2027, respectively. In the Reinforce mitigation (RFM) scenario (with fewer emissions from fuels and a higher share of clean energy generation), both EIPs are expected to achieve carbon peaking by 2025. Finally, we proposed the deep decarbonization of EIPs with different industrial characteristics. This study applied the LEAP model to the EIP scale, explored the paths of deep decarbonization development in different EIPs under the constraint of dual-carbon targets, providing a demonstration for China and other developing countries to achieve the goal of carbon capping in EIPs during rapid industrialization.
•The development of a processing time model for laser scanning of micro-holes with a multi-scale random distribution is outlined.•A discrete gray wolf optimizer has been implemented based exchange, ...shift, and 2-opt algorithms for discrete sequence.•The method of modulating adaptive change rates in the discrete gray wolf optimizer is employed, facilitating rapid iterations on problems of varying scales.•Experimental drilling involving 90, 286, and 649 holes demonstrates a significant reduction in scanning processing time by 38.9%, 53.6%, and 55.5%, respectively.
As an essential technique in fabricating substrates for flexible printed circuit boards, laser drilling of polyimide films facilitates the production of smaller holes of superior quality, which is crucial for preserving the structural integrity and functional reliability of materials. This advanced technology is increasingly critical in sectors demanding high performance, such as high-speed communications and biomedical devices. However, challenges such as the diverse sizes of micro-holes, their irregular placement, and the inherent limitations of scanning components on speed and operational range make efficient and rapid laser drilling a complex endeavor. In this paper, we develop a processing time evaluation model and present a new adaptive discrete grey wolf optimizer (A-D-GWO). The A-D-GWO utilizes exchange, shift, and 2-opt operations to enhance search efficiency and includes an adaptive parameter mechanism, improving its adaptability to various problem sizes and ensuring faster convergence. Experimental results confirmed the algorithm’s effectiveness, reducing processing times by 38.9%, 53.6%, and 55.5% for files containing 90, 286, and 649 holes, respectively. When compared to traditional algorithms, A-D-GWO showed better stability and accuracy, suggesting its suitability for inclusion in laser processing technologies.
In fact, optimizing path within short computation time still remains a major challenge for mobile robotics applications. In path planning and obstacles avoidance, Q-Learning (QL) algorithm has been ...widely used as a computational method of learning through environment interaction. However, less emphasis is placed on path optimization using QL because of its slow and weak convergence toward optimal solutions. Therefore, this paper proposes an Efficient Q-Learning (EQL) algorithm to overcome these limitations and ensure an optimal collision-free path in less possible time. In the QL algorithm, successful learning is closely dependent on the design of an effective reward function and an efficient selection strategy for an optimal action that ensures exploration and exploitation. In this regard, a new reward function is proposed to initialize the Q-table and provide the robot with prior knowledge of the environment, followed by a new efficient selection strategy proposal to accelerate the learning process through search space reduction while ensuring a rapid convergence toward an optimized solution. The main idea is to intensify research at each learning stage, around the straight-line segment linking the current position of the robot to Target (optimal path in terms of length). During the learning process, the proposed strategy favors promising actions that not only lead to an optimized path but also accelerate the convergence of the learning process. The proposed EQL algorithm is first validated using benchmarks from the literature, followed by a comparison with other existing QL-based algorithms. The achieved results showed that the proposed EQL gained good learning proficiency; besides, the training performance is significantly improved compared to the state-of-the-art. Concluded, EQL improves the quality of the paths in terms of length, computation time and robot safety, furthermore outperforms other optimization algorithms.
•The mobile robot path optimization problem is handled and modeled.•An Efficient Q-Learning (EQL) algorithm is proposed.•In EQL, a new definition of states space and actions space is proposed.•New reward function is proposed to initialize Q-table.•Learning process is sped up by exploiting a new efficient selection strategy.•Results on benchmarks from literature demonstrate EQL efficiency and superiority.
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